Cáncer de mama

El cáncer de mama es el tipo de cáncer más frecuente entre las mujeres a nivel mundial (Sung et. al., 2021). Representa 1 de cada 4 casos de cáncer en las mujeres y es responsable de 1 de cada 6 muertes por cáncer. Es la forma más frecuente de cáncer en la mayoría de los países, ocupando el primer lugar en incidencia en 159 de 185 países y en mortalidad en 110 países (Sung et al., 2021). La mayoría de los casos se dan en mujeres mayores de 50 años, si bien también puede afectar a mujeres más jóvenes. Otros factores de riesgo incluyen la predisposición genética, los antecedentes familiares, el inicio temprano de la menstruación, la terapia de reemplazo hormonal, el consumo de alcohol y la obesidad (Łukasiewicz et al., 2021).

La mama está compuesta por lobulillos productores de leche, un sistema de conductos de transporte, y tejido adiposo (Bazira et al., 2021). Todos los cánceres de mama se originan en las células que recubren las unidades lobulillares del conducto terminal (la unidad funcional de la mama) de los túbulos colectores. El tipo más frecuente de cáncer de mama en los hombres es el carcinoma ductal invasivo, que se inicia en los en los conductos galactóforos e invade los tejidos circundantes (Harbeck et al., 2019). El desarrollo del cáncer de mama implica la presencia de mutaciones genéticas que provocan una proliferación celular descontrolada, así como los genes BRCA1 y BRCA2, que participan en la reparación del ADN (Harbeck et al., 2019). Los receptores de estrógenos y progesterona desempeñan un papel importante en la fisiopatología de esta enfermedad: todos los pacientes con tumores que expresan estos receptores deben recibir terapia hormonal para bloquear la actividad del receptor de estrógenos (Harbeck et al., 2019).

El cáncer de mama puede manifestarse de varias maneras. El cuadro clínico más habitual es un bulto en la mama, cambios en el tamaño del pezón, secreción del pezón y cambios en la piel, así como la infección y/o inflamación de la mama (Koo et al., 2017). El cáncer de mama suele ser asintomático en las etapas tempranas, lo que subraya la importancia de llevar a cabo un cribado sistemático (Kalager et al., 2010).

Por lo general se diagnostica mediante pruebas de cribado o como consecuencia de la aparición de un síntoma (dolor o bulto palpable) que motiva la realización de pruebas diagnósticas (McDonald et al., 2016). Estas se complementan con técnicas de imagen para buscar anomalías y caracterizarlas de forma más detallada (McDonald et al., 2016). Normalmente se realiza una biopsia de mama para confirmar la presencia de cáncer cuando se sospecha de esta enfermedad. Esta prueba también permite determinar el tipo específico de cáncer si la lesión resulta ser cancerosa (McDonald et al., 2016). El cáncer de mama se clasifica según la extensión del tumor, la diseminación a los ganglios linfáticos cercanos, la diseminación a sitios distantes, el estado del receptor de estrógeno, el estado del receptor de progesterona, el estado de HER2 y el grado del cáncer (McDonald et al., 2016).

Existen diferentes tipos de cáncer de mama y el tratamiento puede variar según las características moleculares de la enfermedad, el estadio, el tipo de cáncer y el estado de los receptores del paciente (Hong & Xu, 2022). El tratamiento suele implicar una combinación de diferentes terapias y un equipo multidisciplinario de profesionales sanitarios (Hong & Xu, 2022). Las opciones quirúrgicas van desde procedimientos de conservación de la mama hasta la mastectomía, en la que se extirpa la totalidad de la mama (Hong y Xu, 2022). También puede ser necesario extirpar los ganglios linfáticos para evaluar el alcance de la diseminación del cáncer (Hong y Xu, 2022). La radioterapia se emplea con frecuencia después de una terapia de conservación mamaria o una mastectomía en pacientes con factores de riesgo (Hong & Xu, 2022). Asimismo, la quimioterapia sistémica se puede administrar antes o después de la cirugía, según la situación específica de cada paciente (Hong & Xu, 2022). Los cánceres de mama con receptores hormonales positivos se pueden tratar con fármacos que bloquean los efectos del estrógeno y la progesterona. La inmunoterapia es una opción de tratamiento emergente para tratar ciertos cánceres de mama que ayuda al sistema inmunitario a reconocer y atacar las células cancerosas (Hong & Xu, 2022).

Técnicas de diagnóstico por la imagen

Mamografía digital

La mamografía digital es la técnica más utilizada en el cribado del cáncer de mama. Es una técnica de suma bidimensional mediante la cual los rayos X emitidos por un tubo de rayos X son absorbidos en diversos grados por los tejidos y medidos por un detector en el otro extremo. Los tejidos más densos aparecen más brillantes en las imágenes que los tejidos de menor densidad. En esta técnica, las mamas se comprimen mientras se adquiere la imagen para extender el tejido mamario sobre una superficie más amplia (Ikeda, 2011a). De esta forma se reduce la superposición entre los diferentes componentes del tejido mamario, se disminuye la dispersión de los rayos X y se mejora el contraste. Normalmente se obtienen dos imágenes de cada mama: craneocaudal (CC) y mediolateral (MLO) (Ikeda, 2011a).

La mamografía digital es una técnica rápida y útil en el cribado del cáncer de mama, pero tiene sus inconvenientes (Ikeda, 2011a). La compresión mamaria puede resultar dolorosa, y a pesar de la compresión, a menudo se producen artefactos debido a la superposición de diferentes tejidos (Ikeda, 2011a). El cuadrante superior interno de la mama, que es menos móvil ya que está fijado a la pared torácica, es particularmente difícil de visualizar en la mamografía (Ikeda, 2011a). Detectar el cáncer en una mamografía puede ser especialmente complicado en mamas con una gran proporción de tejido denso (Ikeda, 2011a).

Tomosíntesis digital de mama

La tomosíntesis digital de mama (TDM) se basa en la adquisición de imágenes utilizando una fuente de rayos X que se desplaza a lo largo de un arco de rotación. En esta técnica se reconstruyen cortes finos para obtener imágenes en 3D que minimizan la influencia del tejido mamario superpuesto. Es especialmente útil para visualizar lesiones mamarias en un parénquima mamario heterogéneamente denso. En un estudio se observó que la TDM es más sensible en el cribado del cáncer de mama que la mamografía digital (MD). La TDM se puede combinar con la mamografía digital (MD), y en un estudio se ha demostrado que esta combinación mejora la detección del cáncer de mama (Alabousi et al., 2020; Lei et al., 2014; Skaane et al., 2019). Además, la TDM también se puede utilizar en conjunto con la mamografía. Sin embargo, la TDM requiere más tiempo para su adquisición en comparación con la mamografía convencional. Además, la DBT puede verse afectada por el movimiento y otros artefactos (Tirada et al., 2019).

Ecografía

En la ecografía de diagnóstico, un transductor emite ondas sonoras de alta frecuencia que viajan a través de los tejidos, rebotando en ellos y creando "ecos" que se reflejan y detectan en el transductor. A continuación, estos ecos se procesan y se convierten en imágenes en tiempo real en función del tiempo que tardan en ir y volver de los tejidos, que son visualizadas en una pantalla. Es una técnica segura y tiene un coste relativamente bajo que a menudo se utiliza como complemento de la mamografía (Ikeda, 2011b), especialmente para evaluar de forma más exhaustiva un hallazgo palpable o mamográfico.

Incluso puede utilizarse como modalidad de cribado primario en mujeres menores de 30 años o en mujeres embarazadas o lactantes (Dixon, 2008; Ikeda, 2011b). La ecografía es muy útil para aclarar si una masa es quística o sólida, qué tipo de márgenes tiene y su vascularización (Dixon, 2008; Ikeda, 2011b). También contribuye a detectar otras masas y ganglios linfáticos axilares sospechosos (Dixon, 2008; Ikeda, 2011b). Su principal inconveniente es que la calidad de la exploración depende en gran medida del operador (Dixon, 2008; Ikeda, 2011b).

Resonancia magnética

La resonancia magnética utiliza un potente campo magnético y una serie de ondas de radiofrecuencia para perturbar los núcleos de hidrógeno en los tejidos, lo que permite crear imágenes transversales detalladas del cuerpo (Daniel & Ikeda, 2011; Mann et al., 2019). Debido a que los tejidos con diferentes composiciones responden de manera diversa a esta perturbación, la RM es capaz de detectar incluso diferencias sutiles entre los tipos de tejido blando, por lo que se considera la técnica más sensible para diagnosticar el cáncer de mama (Daniel & Ikeda, 2011; Mann et al., 2019). Se utiliza principalmente para realizar cribados en pacientes de alto riesgo que presentan factores de riesgo genéticos o adquiridos (Daniel & Ikeda, 2011).

La RM mamaria requiere disponer de bobinas mamarias dedicadas que transmiten las ondas de radiofrecuencia y reciben la señal generada. Las imágenes suelen adquirirse con una resolución espacial en el plano de 1 mm, un grosor de corte inferior a 3 mm y supresión de la señal de la grasa. Las secuencias de RM empleadas con mayor frecuencia incluyen imágenes ponderadas en T2, imágenes ponderadas en difusión y la RM dinámica con contraste. Para reducir los falsos positivos debidos a cambios inespecíficos del parénquima mamario, es mejor realizar la RM entre los días 7 y 13 del ciclo menstrual (Daniel & Ikeda, 2011). A diferencia de la mamografía, la RM no implica el uso de radiación ionizante y genera imágenes tridimensionales que facilitan la detección de lesiones muy pequeñas (DeMartini & Lehman, 2008; Shahid et al., 2016). La RM también permite evaluar la pared torácica de una forma más detallada que la mamografía y la ecografía (DeMartini y Lehman, 2008). La RM mamaria presenta ciertos inconvenientes, como una baja sensibilidad para detectar microcalcificaciones, su elevado coste y el hecho de estar contraindicada en personas con determinados implantes metálicos (Daniel & Ikeda, 2011).

Retos del cribado y el diagnóstico

A pesar de la evidencia que respalda los beneficios generales del cribado del cáncer de mama (Dibden et al., 2020; Kalager et al., 2010; Tabár et al., 2019), esta práctica también presenta diversos retos técnicos y logísticos. Más de la mitad de las mujeres exploradas en los programas de cribado cada año durante 10 años presentarán una prueba falsa positiva (Hubbard et al., 2011). Esto tiene consecuencias importantes y de gran alcance, como la carga física y emocional que suponen las biopsias innecesarias y el aumento de los costes sanitarios (Nelson, Pappas, et al., 2016; Ong & Mandl, 2015). Además, las pruebas de cribado a menudo no son capaces de detectar el cáncer de mama, especialmente en mujeres con mamas densas (Banks et al., 2006).

Para llevar a cabo el cribado del cáncer de mama, se requiere la participación de profesionales altamente cualificados, como radiólogos y radiógrafos. Sin embargo, actualmente existe una escasez mundial de estos especialistas (Moran & Warren-Forward, 2012; Rimmer, 2017; Wing & Langelier, 2009). Este problema se ve agravado debido a que, en muchos países europeos, el protocolo de referencia en el cribado del cáncer de mama consiste en que cada exploración sea leída por dos radiólogos de forma independiente (Giordano et al., 2012). Además, en algunos países, como en los Estados Unidos, los requisitos para poder interpretar mamografías son muy estrictos debido a las estrictas normas de certificación profesional (Food and Drug Administration, 2001).

En todo el mundo, también existen importantes obstáculos para la adopción de pruebas de cribado del cáncer de mama. Entre ellos se incluyen la falta o el difícil acceso a programas de cribado, la falta de conocimientos o la incomprensión de los beneficios de estos programas y las barreras socioculturales (Mascara & Constantinou, 2021).

El papel de la inteligencia artificial

Mejoras en el ámbito técnico

Hasta ahora, son pocos los estudios publicados en los que se ha investigado directamente el uso de la IA para introducir mejoras técnicas en las exploraciones de mama. Actualmente existe una aplicación comercial que proporciona información en tiempo real a los radiólogos sobre la idoneidad de la posición de la paciente en las mamografías. (Volpara Health, 2022). Otras aplicaciones de la IA se han centrado en reducir las dosis de radiación (J. Liu et al., 2018), mejorar la reconstrucción de imágenes (Kim et al., 2016) y reducir el ruido y los artefactos en la TDM (Garrett et al., 2018).

En el cribado del cáncer de mama, suele combinarse la TDM con la mamografía digital. Sin embargo, es importante tener en cuenta que ello duplica la dosis de radiación que recibe la paciente (Svahn et al., 2015). Para evitarlo, existe un interés cada vez mayor en generar mamografías sintéticas a partir de datos de TDM (Chikarmane et al., 2023). En un extenso estudio prospectivo realizado en Noruega, se encontró que la precisión de la TDM combinada con la mamografía digital o la mamografía sintética para la detección del cáncer de mama fue muy similar.(Skaane et al., 2019). En investigaciones recientes, se ha explorado la posibilidad de mejorar la calidad de la mamografía sintética mediante el uso de la IA, y los resultados han sido prometedores (Balleyguier et al., 2017; James et al., 2018).

Mejoras en el diagnóstico

Evaluación de la densidad mamaria

El tejido mamario denso que se observa en la mamografía representa tejido fibroglandular. Las mujeres con mamas densas tienen un riesgo de 2 a 4 veces mayor de sufrir cáncer de mama que las mujeres con mamas con una mayor cantidad de tejido mamario graso ( Byrne et al., 1995; Duffy et al., 2018; Torres-Mejía et al., 2005). Además, la sensibilidad de la mamografía en el cáncer de mama es un 20-30 % menor en mamas densas que en mamas menos densas (Lynge et al., 2019). Para evaluar la densidad mamaria se utiliza la clasificación BI-RADS (Berg et al., 2000).

Varios estudios de gran envergadura han explorado el potencial de evaluar automáticamente la densidad mamaria en mamografías mediante el uso de herramientas basadas en la IA. Una red neuronal convolucional (CNN) entrenada con 14 000 mamografías y probada en casi 2000 mamografías clasificó la densidad mamaria en “densidad dispersa” o “heterogéneamente densa”, con un área bajo la curva (AUC) de 0,93 (Mohamed et al., 2018). Otro estudio utilizó una CNN capaz de realizar una clasificación BI-RADS binaria y de cuatro vías y entrenada en más de 40 000 mamografías (Lehman et al., 2019). En un conjunto de datos que incluyó más de 8000 mamografías, se observó una buena concordancia en la clasificación de la densidad mamaria entre el algoritmo y los radiólogos individuales (kappa = 0,67), así como también con el consenso de cinco radiólogos (kappa = 0,78) (Lehman et al., 2019).

Detección del cáncer de mama

En una revisión sistemática que incluyó 82 estudios que utilizaron IA para la detección del cáncer de mama con varios estándares de referencia, los autores hallaron un AUC de 0,87 para la mamografía, 0,91 para la ecografía, 0,91 para la TDM y 0,87 para la RM (Aggarwal et al., 2021). Estos son resultados prometedores; sin embargo, las comparaciones directas entre algoritmos basados en IA y radiólogos revelan que todavía hay margen de mejora. En otra revisión sistemática que incluyó estudios que utilizaron como referencia la histopatología o el seguimiento (para mujeres con pruebas negativas en el cribado), se halló que el 94 % de las 36 CNN identificadas presentaron una menor precisión que un solo radiólogo. Además, todas las CNN presentaron una menor precisión que el consenso de 2 o más radiólogos cuando se utilizaron como un sistema independiente (Freeman et al., 2021). Así pues, la evidencia actual no respalda el uso de la IA como una estrategia independiente para la detección del cáncer de mama.

Predicción del cáncer de mama

La IA ha demostrado ser prometedora en la predicción del riesgo de desarrollar cáncer de mama a partir de mamografías de cribado, ya sea mediante una mejor evaluación de la densidad mamaria, un factor de riesgo establecido para el cáncer de mama (Duffy et al., 2018), o mediante la detección de características de imagen sutiles que son indicadores tempranos de cáncer (Batchu et al., 2021). Varios estudios han utilizado modelos basados en IA para predecir el riesgo de desarrollar cáncer de mama en el futuro basándose en mamografías (Batchu et al., 2021; Geras et al., 2019).

Una CNN entrenada con casi 1 000 000 de imágenes mamográficas mostró un AUC de 0,65 para predecir el desarrollo futuro del cáncer de mama en comparación con el 0,57-0,60 de las puntuaciones de densidad mamaria basadas en mamografías convencionales (Dembrower, Liu, et al., 2020). Un estudio de menor envergadura halló un AUC de 0,73 para un método basado en CNN para predecir el cáncer de mama a partir de imágenes mamográficas normales. (Arefan et al., 2020). Otro algoritmo de aprendizaje profundo presentó un AUC de 0,82 para predecir cánceres de intervalo (cánceres detectados en los 12 meses posteriores a la realización de una mamografía con resultado negativo) en comparación con el 0,65 para la evaluación visual de la densidad mamaria según la clasificación BI-RADS (Hinton et al., 2019). Otro modelo basado en aprendizaje profundo que incorporó tanto los factores de riesgo como hallazgos mamográficos para predecir el riesgo de cáncer de mama obtuvo un AUC de hasta 0,7, superando la precisión de los modelos predictivos basados únicamente en factores de riesgo o hallazgos mamográficos. (Yala, Lehman, et al., 2019).

Mejora en la eficiencia

La gran cantidad de mamografías realizadas y la escasez de radiólogos cualificados han convertido las mejora en la eficiencia en un área de investigación sumamente interesante para la aplicación de la IA en el cáncer de mama.

En un estudio, los autores simularon un flujo de trabajo en el que las mamografías eran interpretadas por un radiólogo y un modelo de aprendizaje profundo, y la decisión se consideraba definitiva si ambos estaban de acuerdo (McKinney et al., 2020). Solo se consultó a un segundo radiólogo en caso de desacuerdo, lo que se asoció con una reducción del 88 % en la carga de trabajo del segundo radiólogo con un valor predictivo negativo superior al 99,9 % (McKinney et al., 2020).

En un estudio clínico sin precedentes realizado en Suecia, se asignó aleatoriamente a aproximadamente 80 000 mujeres para que una CNN evaluara previamente, o no, sus mamografías de cribado (Lång et al., 2023). En el grupo de intervención, solo las mamografías que obtuvieron una puntuación de alta probabilidad de malignidad fueron revisadas dos veces (las demás fueron evaluadas por un radiólogo). A continuación, los resultados se compararon con la doble revisión convencional realizada sin la ayuda del algoritmo. En un análisis intermedio de los datos de las 80 000 mujeres, ambos grupos del estudio presentaron una tasa idéntica de falsos positivos del 1,5 %. En el grupo de intervención, el valor predictivo positivo para las llamadas para repetir el estudio fue del 28,3 %, mientras que en el grupo de control fue del 24,8 %. Además, la estrategia implementada redujo la carga de trabajo en un 44,3 % (Lång et al., 2023).

En otros estudios, se ha utilizado la IA para preseleccionar las mamografías, identificando aquellas con una baja probabilidad de cáncer y mostrando a los radiólogos únicamente las que presentan una alta probabilidad de cáncer. En un estudio realizado en Estados Unidos, se utilizó un flujo de trabajo simulado en el que intervino una CNN entrenada con más de 212 000 mamografías y probada en más de 26 000 casos (Yala, Schuster, et al., 2019). El algoritmo presentó una sensibilidad no inferior al cáncer de mama (90,1 % frente a 90,6 %) y una especificidad ligeramente superior en comparación con los radiólogos (94,2 % frente a 93,5 %). Asimismo, se asoció con una carga de trabajo un 19,3 % menor (Yala, Schuster, et al., 2019). En un estudio de menor envergadura realizado en España se observó una reducción del 72,5 % en la carga de trabajo utilizando IA para clasificar solo los casos de TDM de alto riesgo para que los revisara un segundo radiólogo, y del 29,7 % utilizando IA para clasificar solo los casos de DM de alto riesgo para que los revisara un segundo radiólogo, en comparación con los flujos de trabajo tradicionales de doble revisión mamográfica (Raya-Povedano et al., 2021). También se halló que esta estrategia para clasificar mediante IA los casos de mamografía y TDM para llevar a cabo una segunda revisión ofrece una sensibilidad no inferior en comparación con los flujos de trabajo de la doble revisión estándar de la mamografía y la TDM (Raya-Povedano et al., 2021). En un estudio sueco, se utilizó una estrategia similar con un algoritmo de IA disponible comercialmente. Este algoritmo arrojó una tasa de falsos negativos no superior al 4 % y demostró la capacidad de detectar 71 cánceres adicionales por cada 1000 exploraciones en comparación con una doble lectura negativa realizada por radiólogos humanos en pacientes consideradas de alto riesgo por el algoritmo de IA (Dembrower, Wåhlin, et al., 2020).

En un estudio de más de un millón de mamografías en ocho centros de cribado y tres fabricantes de dispositivos, un algoritmo de aprendizaje profundo disponible comercialmente clasificó el 63 % para no realizar más estudios basándose en evaluaciones de alta confianza de las exploraciones (Leibig et al., 2022). Las exploraciones en las que la confianza del algoritmo era baja se mostraron a los radiólogos. Esta estrategia mejoró la sensibilidad de los radiólogos (en comparación con la lectura sin ayuda) entre un 2,6 % y un 4 %, y la especificidad entre un 0,5 % y un 1,0 % (Leibig et al., 2022).

Retos y aplicaciones futuras

El uso de la inteligencia artificial en el cribado del cáncer de mama presenta varios desafíos éticos, técnicos y metodológicos, que proporcionan un marco para guiar las investigaciones futuras en este campo (Hickman et al., 2021).

Hasta ahora, la mayoría de las herramientas basadas en IA se han enfocado en la mamografía digital (Aggarwal et al., 2021). Sin embargo, otras técnicas de exploración, como la TDM y la RM, ofrecen ventajas únicas (Alsheik et al., 2019; Mann et al., 2019) y es probable que desempeñen un papel más importante en el cribado del cáncer de mama en el futuro. No obstante, debido a que la TDM y la RM son técnicas tomográficas que producen resultados tridimensionales, procesarlos utilizando herramientas basadas en IA requerirá un mayor espacio de almacenamiento y una mayor potencia computacional (Prevedello et al., 2019).

La incidencia, la presentación y el desenlace del cáncer de mama están relacionados con varios factores sociodemográficos, incluida la raza y el origen étnico (Hirko et al., 2022; Hu et al., 2019; Martini et al., 2022). Entrenar herramientas basadas en IA en conjuntos de datos que representen una población diversa es clave para garantizar que puedan generalizarse y beneficiar a la mayor cantidad de personas posible.

El rendimiento general de la IA para la detección del cáncer de mama ha sido impresionante. Sin embargo, cabe destacar que en un estudio no se pudo demostrar una sensibilidad no inferior de la IA a la de los radiólogos en la detección del cáncer de mama (Lauritzen et al., 2022). Además, la calidad de la evidencia detrás de muchos estudios sobre este tema es preocupante. Una revisión sistemática en la que investigó la precisión de las herramientas basadas en la IA para la detección del cáncer de mama identificó varias áreas de posible mejora (Freeman et al., 2021). La revisión no encontró estudios prospectivos y los estudios identificados presentaban deficiencias en su calidad metodológica. En particular, los autores observaron que los estudios más pequeños mostraron resultados más positivos que no se replicaron en estudios de mayor envergadura. En otra revisión sistemática se halló que solo alrededor de una décima parte de los estudios utilizó un conjunto de datos externos para la validación. Además, ningún estudio proporcionó un cálculo del tamaño de la muestra preespecificado, y se identificaron problemas graves de sesgo en la selección y estándares de referencia inadecuados (Aggarwal et al., 2021). Estos problemas metodológicos podrían mitigarse en el futuro mediante la introducción de grandes repositorios de datos abiertos (Nguyen et al., 2023) y una mayor adherencia a las directrices para realizar investigaciones médicas basadas en IA (Lekadir et al., 2021; X. Liu et al., 2020).

Conclusión

La integración de la inteligencia artificial en los programas de cribado del cáncer de mama es prometedora para mejorar la calidad de la imagen, mejorar la eficiencia y predecir el riesgo futuro de esta enfermedad. Para detectar el cáncer de mama en exploraciones de cribado, la evidencia indica que la IA permite obtener unos mejores resultados en sinergia con los radiólogos. Resulta crucial seguir investigando para abordar los retos asociados al uso de la IA en el cribado del cáncer de mama, incluida la ampliación de sus aplicaciones más allá de la mamografía y la garantía de su uso ético y responsable. Con la evolución continua de las aplicaciones de IA, el futuro del cribado del cáncer de mama tiene un inmenso potencial de mejora de la accesibilidad, intervención precoz y, en última instancia, mejores resultados para las pacientes.

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Artificial Intelligence in medical imaging: What, How and Why?

Introduction

Artificial intelligence (AI) is a field that enables computer systems to solve problems by adapting to changing circumstances, often by mimicking human reasoning and judgement. Several demographic and healthcare trends are driving the use of AI in medical imaging. The amount of medical imaging data being acquired is steadily increasing (Larson et al., 2011; Smith-Bindman et al., 2008, 2012; Winder et al., 2021). There is also a widespread shortage of healthcare workers (Core Health Indicators in the WHO European Region 2015. Special Focus: Human Resources for Health, 2017) with an ever-increasing workload (Levin et al., 2017), and the number of medical imaging examinations is expected to grow exponentially over the next two decades (Tsao, 2020). Radiologists and radiology technologists are in particularly scarce supply (AAMC Report Reinforces Mounting Physician Shortage, 2021, Clinical Radiology UK Workforce Census 2019 Report, 2019). Finally, the ageing world population (Population Ages 65 and above, n.d.; WHO, n.d.-a) and an increasing global burden of chronic illnesses (WHO, n.d.-b) are expected to compound these problems in the near future.

Broadly speaking, the advantages of AI in medical imaging could potentially include the ability to provide insights that would otherwise not be possible using traditional methods (such as humans looking at images) and to may do so in a faster and automated way (without the need for human interaction). AI-based solutions in medical imaging could improve and accelerate the detection of disease, generate in-depth risk assessment of disease development and progression, and may reduce subjectivity in the interpretation of medical imaging data.

The current state of AI in medical imaging

Over the past few years, the landscape of AI in medical imaging has changed dramatically. Many promising applications have arisen, the field has seen an unprecedented surge in funding, and we have seen positive trends in the adoption of AI solutions by radiologists, as well as their approval by regulatory bodies.

Applications

Although radiology departments provide a plethora of services, the core service provided is the imaging study. Applications of AI in medical imaging can therefore be categorized into those applied either before, during, or after the imaging study.

Before Image Acquisition

Several steps have to take place within the context of a radiology department’s workflow before a patient is undergoing imaging study. AI applications that aim to improve these steps are referred to as “upstream AI” and could potentially increase efficiency and provide more personalized decision making in a radiology department.

Missed medical appointments are common, reduce the efficiency of hospitals, and waste resources (Dantas et al., 2018). Studies from Japan (Kurasawa et al., 2016) and the United Kingdom (Nelson et al., 2019) have shown that AI can be used to predict no-shows with high accuracy. This allows the use of targeted strategies to reduce the likelihood of a patient missing their appointment, including sending automated reminders.

One of the most important decisions made in the radiology department is the exact scan protocol to use on a given patient. While this applies to all imaging modalities, the widest range of choice is seen with magnetic resonance imaging (MRI). This includes choosing the appropriate set of sequences and making decisions about whether or not to administer intravenous contrast agents. Natural language classifiers that interpret the narrative text of the clinician’s scan requests have been used to select appropriate MRI protocols. In one study, a gradient boosting classifier predicted the appropriate MRI brain protocol to use based on the scan request with high accuracy (95 %) (Brown & Marotta, 2018). For musculoskeletal MRI, a deep learning classifier was 83 % accurate in determining the need for a contrast agent (Trivedi et al., 2018). Such applications can substantially improve efficiency by foregoing the time-consuming task of radiologists going through unstructured narrative scan requests written by referring clinicians.

During Image Acquisition

Substantial improvements have recently been made in the use of AI for improving image quality. In a recent survey, radiologists identified the enhancement of image quality as being the most mainstream current use case for AI in medical imaging (Alexander et al., 2020). While earlier attempts at reducing image noise using deep learning techniques were criticized for removing details from the images that jeopardized the visibility of essential features within the images, more recent implementations have made this issue largely obsolete.

In particular, deep learning techniques like generative adversarial networks have shown great potential in image denoising (Wang et al., 2021). Some of these applications target the image reconstruction stage (where the raw sensor data is converted into an interpretable image) providing superior signal-to-noise ratios and reducing image artefacts (Zhu et al., 2018). In lung cancer screening, deep-learning-based image denoising improved both the image quality and the diagnostic accuracy of ultra-low-dose computed tomography (CT) for detecting suspicious lung nodules (Hata et al., 2020; Kerpel et al., 2021). Scans that were 40-60 % acquired faster than standard scans and enhanced with deep-learning-based algorithms were of better image quality than, and similar diagnostic value as, standard scans of the brain (Bash, Wang, et al., 2021; Rudie et al., 2022) and spine (Bash, Johnson, et al., 2021). Similarly, convolutional neural networks can be used to reduce specific CT and MRI artefacts and improve spatial resolution (Hauptmann et al., 2019; K. H. Kim & Park, 2017; Park et al., 2018; Y.Zhang & Yu, 2018).

Reconstruction algorithms based on deep learning have enabled ultra-low-dose computed tomography scans to be acquired while maintaining diagnostic quality. This is of particular benefit in children and pregnant women, where reduction of radiation dose to the absolute minimum is critical. These deep- learning-based CT image reconstruction approaches are associated with lower image noise and better image texture than state-of-the-art alternatives like iterative reconstruction (Higaki et al., 2020; McLeavy et al., 2021; Singh et al., 2020). In positron emission tomography, deep learning can reduce injected tracer dosage by one-third and scan times by up to half while maintaining scan quality (Katsari et al., 2021; Le et al., 2020; Xu et al., 2020).

After Image Acquisition

Radiology technologists and radiologists usually share the task of calling back patients for repeat examinations, but doing so consistently and reliably is exceedingly difficult due to time constraints. Image quality of AI enhanced brain MRI scans has been shown to be equal to or better than conventional scans, even when using acquisition protocols that reduce scan times by 45-60% (Schreiber-Zinaman & Rosenkrantz, 2017).

Prioritizing scan reading on a radiologist’s worklist is often done based on several factors including the type of scan, the referring department, and direct communication with the radiologist about the scan’s urgency. Several approaches have been tested to influence the order in which scans are read to improve efficiency and ensure the most critical scans are seen first. These include assigning different radiologists specific exams based on how quickly they read certain scan types (Wong et al., 2019) and automatically detecting emergent findings on the images and pushing these cases to the “top of the list” (Prevedello et al., 2017; Winkel et al., 2019).

About 70 % of all AI-based solutions in radiology focus on “perception” - a category of functionalities that includes segmentation, feature extraction, as well as detection and classification of pathology (Rezazade Mehrizi et al., 2021). Within this category, the majority of tools extract information from the imaging data with or without quantification as well as draw the user’s attention to potential pathology (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021). Over the past few years, some of the most promising applications in this category have included the detection of brain vessel occlusion, brain haemorrhage, lung nodules, pneumothorax and pleural effusions, fractures, and the characterization of breast lesions.

Funding

The total amount of investment in AI-based medical imaging companies amounted to $ 1.17 billion between 2014 and 2019 (Alexander et al., 2020). In the same period, the number of companies in this space tripled, leading to a drop of almost 30 % in the median investment in each company (Alexander et al., 2020). Between 2019 and 2020, private investment in AI companies increased by 9.3 % (D. Zhang et al., 2021). By 2030, investment in AI-based solutions in medical imaging is expected to exceed $3 billion (Tsao, 2020).

Adoption

There have been positive trends in the adoption of AI tools by radiologists and radiology technologists over the past few years. Between 2015 and 2020, AI use in radiology departments went up by 30 %, according to a survey of 1,861 radiologists conducted by the American College of Radiology (ACR) (Allen et al., 2021).

Despite this promising trend, the adoption of AI tools is widely considered to be disproportionately low relative to the amount of funding, the number of companies, and the perceived promise of these tools. The ACR survey provides some insight into why and offers a starting point for developing strategies to improve AI adoption.

Almost three-quarters of radiologists who were not using AI had no plans to do so in the future because they either were not convinced of its benefits or did not think the associated costs were justified (Allen et al., 2021).

Similar results have been found in other studies, with radiologists citing scepticism in the capabilities of AI tools and the fact that relatively few have regulatory approval as reasons for not adopting them in their practice (Alexander et al., 2020).

Regulatory success

Until August 2019, 60 % of available AI-based radiology solutions had no regulatory approval (Rezazade Mehrizi et al., 2021). As of April 2020, a total of 100 AI solutions had a CE mark, a prerequisite for them to be made commercially available as medical devices in Europe (van Leeuwen et al., 2021). As of the time of writing, more than 150 AI solutions have gained FDA clearance (AI Central, n.d.). Several useful databases of approved or cleared AI-based solutions in medical healthcare are currently available (AI Central, n.d., AI for Radiology, n.d., Medical AI Evaluation, n.d., The Medical Futurist, n.d.)

The future of AI in medical imaging

The past few years have seen exponential growth in the interest in AI in medical imaging, both in terms of the amount of research and the amount of money being invested in the field. This interest runs the gamut of the radiology workflow, but “perception” applications - for the quantification of biomarkers and the detection of disease processes - have dominated so far. In the radiology community, trends have shifted from AI being perceived as an unwelcome intruder to increased adoption, albeit with some scepticism and hesitation regarding its value. The first AI solutions in medical imaging were granted regulatory approval, and we have seen the first indications of how such solutions may be reimbursed.

New directions

With increasing acknowledgement that a large proportion of AI’s potential in medical imaging may lie in “upstream” or “non-interpretative” applications, the field is likely to expand its focus in the coming years. This will include more research into applications that improve the efficiency of radiology workflows and provide more personalized patient care (Alexander et al., 2020). AI is likely to become more involved even earlier in the patient management process - i.e. before the clinician decides that a diagnostic image test is necessary. Such applications, essentially clinical decision support systems, have successfully been used for decision-making about treatments in several settings (Bennett & Hauser, 2013; Komorowski et al., 2018), successfully used in treatment decision making (Bennett & Hauser, 2013). In the future, AI solutions may draw clinicians’ attention to the need for further imaging tests based on reviewing the patient’s clinical information, laboratory tests, and prior imaging tests (Makeeva et al., 2019).

The vast majority (77-84 %) of currently available AI solutions in medical imaging target CT, MRI and plain radiographs (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021). Nuclear imaging techniques, such as positron emission tomography (PET). provide unique information not readily gained from other modalities. PET has thus far been largely neglected in terms of AI research (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021), and is thus a potentially promising avenue for the field’s expansion.

AI research is also expected to undergo a shift in the type of data being used. The typical inpatient receives more than one imaging study during their hospital stay (Shinagare et al., 2014). Despite this, only about 3 % of current AI-based radiology solutions combine data from multiple modalities (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021). Combining data from multiple imaging sources may improve the diagnostic capabilities of AI solutions. Furthermore, future AI solutions in radiology are likely to combine imaging information, clinical information, as well as non- imaging diagnostic tests (Huang et al., 2020). By doing this, AI solutions may be able to identify patterns in the data collected during a patient’s hospital stay that may not be readily identifiable by healthcare workers (Rockenbach, 2021). This could ultimately lead to more accurate diagnoses and could help inform better and more personalized treatment decisions.

The expectations for AI-based medical imaging solutions are also likely to shift from the current focus of triage, image enhancement and automation. With increasing algorithmic complexity, data availability, and experience with these tools, this shift may lead to AI solutions reaching specific diagnoses and recommending specific steps in a patient’s management plan. Similar to how the introduction of the first AI tools for image screening and processing around 2018 spurred investment in the field, marketing analyses predict a similar investment boost in the next few years as AI tools providing specific diagnoses and management steps become more widespread (Michoud et al., 2019).

One important criticism of the current, arguably still nascent, landscape of AI in medical imaging is that it is too fragmented. Radiology professionals would likely welcome a more streamlined integration of AI solutions in their daily workflow. This includes seamless integration of these solutions into established radiology workflows, with as much as possible happening “in the background” without user input. Furthermore, the outputs of these solutions could be integrated into available radiological information systems. Consequently, the field could move from the plethora of currently available niche AI solutions, each targeted towards a single very specific application, to broader software suites that perform many different functions for a given imaging modality or body region.

The fragmented investment in the AI in medical imaging market (Alexander et al., 2020) fosters innovation, allowing many players to test out different strategies in this emerging field. However, in the long term, consolidation may increase adoption and stimulate the kind of seamless integration into existing workflows that is needed, allowing fewer companies to offer these solutions at scale (Alexander et al., 2020).

Challenges

Quality and reporting of evidence

In a review of 100 CE-marked AI solutions, 64 % of them had no peer-reviewed scientific evidence for their efficacy (van Leeuwen et al., 2021). Where there was scientific evidence, the level was low, rarely exceeding the demonstration of diagnostic accuracy (van Leeuwen et al., 2021). Another systematic review of the evidence for deep learning algorithms in medical imaging found a generally high diagnostic accuracy, albeit with a high risk of bias across studies (Aggarwal et al., 2021). The main sources of bias include the lack of external validation (D. W. Kim et al., 2019; Liu et al., 2019), insufficiently detailed reporting of results (Liu et al., 2019), retrospective study design (Nagendran et al., 2020), and the inaccessibility of data and code to reviewers and readers (Nagendran et al., 2020).

Overall, studies on AI tools have shown a worrying lack of standardized reporting and adherence to recommended reporting guidelines (Aggarwal et al., 2021; Yusuf et al., 2020). This is despite the fact that several extensions to established reporting guidelines, as well as AI-specific guidelines, are currently available (Shelmerdine et al., 2021). Widespread implementation of these guidelines should be a focus of AI developers in the future.

AI developers should also be cognizant that the currently “acceptable” level of evidence for AI-based solutions is likely to become obsolete in the near future. Both regulators and potential users will likely demand higher levels of evidence for these solutions, akin to the evidence required for new pharmaceutical drugs. In the next few years, will see more of these AI solutions being tested in randomized clinical trials. In the more distant future, it is plausible that such expectations will go beyond providing evidence of the safety, efficacy, or diagnostic performance of these solutions, to the demonstration that they provide added monetary or societal value.

Rising up to the challenge of improving the quality and reporting of evidence for AI-based solutions may pay off in the long run. It could reduce the risk of bias in AI studies, could allow the thorough and transparent assessment of study quality by potential users and regulators, and could facilitate systematic reviews and meta-analyses. These steps may increase the trust in, and uptake of, AI-based solutions and ensure that they offer realistic, sustainable improvements in people’s lives.

Regulation

Several aspects inherent to AI pose challenges to attempts at regulating it like other healthcare interventions. The inner workings of AI solutions are often opaque and difficult to comprehensively describe in a manner traditionally expected by regulatory bodies.

The past few years have shown us that these regulatory challenges are far from intractable. Both the Food and Drug Administration and the European Commission have recently proposed initial regulatory frameworks for AI solutions (Center for Devices & Radiological Health, 2021; European Commission, 2021).

In part as a response to the transparency necessary for regulatory approval, researchers have made substantial progress in making AI’s decision-making more understandable and explainable. This movement towards “interpretable AI” will gain further impetus in the near future as reliance on AI for real-world clinical decision-making increases.

This has many advantages, including making regulatory approval easier, increasing trust in these solutions by users, minimizing biases, and improving the reproducibility of these solutions (Holzinger et al., 2017; Kolyshkina & Simoff, 2021; “Towards Trustable Machine Learning,” 2018; Yoon et al., 2021).

Data privacy

From development and testing to implementation, AI solutions in medical imaging require access to patient data. This has raised concerns about data privacy, which is a multifaceted and highly complex issue (Murdoch, 2021) that is prominently represented in the regulatory pathways of different countries (COCIR, the European Coordination Committee of the Radiological, Electromedical and Healthcare IT Industry, 2020). Suggested solutions to the data privacy question have ranged from those focusing on oversight to more technical approaches.

The patients providing the data have to be made aware that they are doing so, as well as be informed about why and how their data will be used (Lotan et al., 2020), as explicitly stipulated in the EU’s General Data Protection Regulation (GDPR) (General Data Protection Regulation (GDPR) – Official Legal Text, 2016). Considering the fast-paced nature of the development of AI solutions, whether patients can be kept sufficiently informed as these algorithms are continuously retrained has been questioned (Kritikos, 2020). While fully anonymized data is not subject to such strict requirements under the GDPR (What Is Personal Data?, 2021), anonymization is exceedingly difficult to achieve for medical imaging data.

The data privacy issue will have to be approached on several fronts. In addition to legislation governing the use of patient data, it is becoming increasingly clear that everyone involved in the development and use of AI solutions - developers, payers, regulatory bodies, researchers and radiologists - has a role to play in ensuring that the data is protected and used responsibly.

Moreover, the next few years will likely see further research into technical approaches to strengthen data protection. These include better ways to reduce the chances of data being traced back to individuals, methods for keeping sensitive data stored locally even when the algorithm being trained is hosted in some “central” location, data perturbation to minimize the information within a given dataset pertaining to individual patients, and data encryption (G. Kaissis et al., 2021; G. A. Kaissis et al., 2020).

Democratization

If AI in medical imaging is to live up to its potential, the algorithms being developed have to work for everyone. This “democratization” of AI involves ensuring that healthcare providers have the knowledge and skills needed to use AI-based solutions. With a few exceptions (Paranjape et al., 2019), medical student curricula currently include little to no dedicated education about AI (Banerjee et al., 2021; Blease et al., 2022). Surveys from around the world have shown that medical students’ and doctors’ (Ahmed et al., 2022; Bisdas et al., 2021; Collado-Mesa et al., 2018; Kansal et al., 2022; Pinto Dos Santos et al., 2019; Sit et al., 2020) exposure to AI during training was low despite the high demand for more AI education (Kansal et al., 2022; Ooi et al., 2021; Sit et al., 2020). In addition, there are still large differences between genders and countries in the perceived knowledge about AI amongst medical students (Bisdas et al., 2021). There are many reasons for these differences and many challenges associated with the widespread integration of AI education into healthcare training curricula. In the coming years, strategies to tackle these issues should be investigated to ensure that future healthcare providers are equipped with the knowledge and skills they need to work in an environment where AI plays a growing role.

Democratization also involves ensuring that patients of different genders, lifestyles, ethnicities, and geographical locations can benefit from AI-based solutions. For this to happen, these solutions have to be accessible and their performance generalizable. The latter requires the acquisition of diverse data from multiple institutions, preferably from multiple countries, for training AI-based solutions. It also requires the implementation of safeguards to ensure that sources of bias throughout the development process are not propagated to the trained algorithm (Vokinger et al., 2021), an issue that has only recently come to the forefront (Larrazabal et al., 2020; Obermeyer et al., 2019; Seyyed-Kalantari et al., 2021).

Reimbursement

As countries’ policies for regulating AI in healthcare gradually begin to take shape, one important aspect that needs attention is who will pay for these AI solutions, and according to what framework.

Many consider Germany’s 2020 Digital Supply Act a step in the right direction for reimbursement of digital health solutions. Under this policy, digital applications prescribed by physicians are reimbursable by statutory health insurance if they are proven to be safe, be compliant with data privacy statutes, and improve patient care. The UK, on the other hand, has released a guide for potential buyers of AI-based solutions, which serves as a starting point for companies to prepare for reimbursement applications (A Buyer’s Guide to AI in Health and Care, 2020).

Thus far, reimbursement success stories in the digital health space have been few and far between (Brink- mann-Sass et al., 2020; Hassan, 2021). This is in part due to requirements varying greatly by country (COCIR, the European Coordination Committee of the Radiological, Electromedical and Healthcare IT Industry, 2020). In general, providers of digital health solutions will need to provide evidence for the overall value that these solutions bring, including detailed health economics studies showing potential cost savings.

Radiology’s position as a service provider to multiple hospital departments means that AI-based solutions in this space will be expected to show a far-reaching impact (van Duffelen, 2021). Companies will need to show short-term value (e.g. faster/better image reading and reporting) as well as long-term value (e.g. early diagnosis and treatment, disease prevention, reduction in unnecessary follow-up). The coming years will see companies compete to demonstrate such impact, while at the same time experimenting with different pricing models and navigating the evolving bureaucratic reimbursement landscape.

Conclusion

Over the past few years, the field of AI in medical imaging has undergone a rapid but steady transformation. AI can now achieve things in radiology that few people thought possible a mere decade ago. The field is also gradually overcoming one of its most significant perceived hurdles - regulatory approval. In addition, while fear and scepticism dominated radiologists’ perception of the future of AI in their speciality a few years ago, this is no longer the case.

The massive progress and interest in the field of AI in medical imaging is expected to continue into 2022 and beyond. Several exciting transformations await the field - it will likely expand its focus in the coming years to improve radiology workflow efficiency, involve hitherto neglected imaging modalities, combine data from multiple modalities, and provide more concrete diagnostic predictions and management recommendations. Easy-to-use and comprehensive software suites utilizing AI will be incorporated into existing radiology workflows, making radiologists’ and radiographers’ work easier and more efficient.

As in any rapidly growing field, several scientific, regulatory, and economic challenges face AI in medical imaging. But the past few years have shown us that even the most difficult problems can be solved. Developers and users of AI-based solutions need to be aware of these issues so that they can adapt their strategies to changing expectations on a regulatory and societal level. Doing this will allow them to thrive in a fascinating field with the potential to improve virtually every aspect of healthcare.

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