Il carcinoma mammario

Il carcinoma mammario è il tipo specifico di cancro più comune tra le donne a livello globale (Sung et. al., 2021). Nella popolazione femminile, il carcinoma mammario rappresenta 1 caso di cancro su 4 e 1 decesso per cancro su 6, classificandosi al primo posto nella stragrande maggioranza dei Paesi (159 su 185 Paesi) per incidenza e in 110 Paesi per mortalità (Sung et. al., 2021). La maggior parte dei casi si verifica nelle donne di età superiore ai 50 anni, ma la malattia può colpire anche le donne più giovani. Altri fattori di rischio includono: predisposizione genetica, anamnesi familiare, menarca precoce, terapia ormonale sostitutiva, consumo di alcol e obesità (Łukasiewicz et al., 2021).

Il seno è composto da lobuli secernenti latte, da un sistema di dotti galattofori e da tessuto adiposo (Bazira et al., 2021). Tutti i carcinomi mammari hanno origine nelle cellule che rivestono le unità terminali dotto-lobulari (l'unità funzionale del seno) dei dotti collettori. Il tipo più comune di carcinoma mammario maschile è il carcinoma duttale infiltrante, che si sviluppa nei dotti galattofori e invade i tessuti vicini (Harbeck et al., 2019). Lo sviluppo del carcinoma mammario è legato a mutazioni genetiche che causano una proliferazione cellulare incontrollata, nonché ai geni BRCA1 e BRCA2 coinvolti nella riparazione del DNA (Harbeck et al., 2019). I recettori degli estrogeni e del progesterone svolgono un ruolo importante a livello fisiopatologico, pertanto tutti i pazienti con tumori che esprimono questi recettori dovrebbero ricevere una terapia ormonale per bloccare l'attività dei recettori degli estrogeni (Harbeck et al., 2019).

Il carcinoma mammario può manifestarsi in diversi modi. La manifestazione clinica più comune è rappresentata da un nodulo al seno, cambiamenti nelle dimensioni del capezzolo, secrezione dal capezzolo e alterazioni cutanee, nonché da infezione e/o infiammazione mammaria (Koo et al., 2017). Il carcinoma mammario in stadio iniziale è spesso asintomatico, per cui è estremamente importante lo screening di routine (Kalager et al., 2010).

Il carcinoma mammario viene generalmente diagnosticato attraverso lo screening o un esame diagnostico eseguito a seguito del rilevamento di un sintomo (dolore o nodulo palpabile) (McDonald et al., 2016). A questi si aggiungono tecniche di imaging per cercare anomalie e caratterizzarle in modo più dettagliato (McDonald et al., 2016). Di solito, in caso di sospetto, viene eseguita una biopsia mammaria per confermare la presenza di cancro, la quale può anche determinarne il tipo specifico se la lesione è cancerosa (McDonald et al., 2016). Il carcinoma mammario viene stadiato in base all'estensione del tumore, alla diffusione ai linfonodi vicini, alla diffusione a sedi distanti, allo stato dei recettori degli estrogeni, allo stato dei recettori del progesterone, allo stato HER2 e al grado del tumore (McDonald et al., 2016).

Esistono diversi tipi di carcinoma mammario e il trattamento può variare in base alle caratteristiche molecolari della malattia, allo stadio, al tipo di cancro e allo stato dei recettori del paziente (Hong & Xu, 2022). Il trattamento di solito prevede una combinazione di diverse modalità e l’intervento di un team multidisciplinare di professionisti sanitari (Hong & Xu, 2022). Le opzioni chirurgiche spaziano dagli interventi con conservazione del seno alla mastectomia, che ne prevede la rimozione integrale (Hong & Xu, 2022). Potrebbe anche essere necessaria l’asportazione di linfonodi per valutare l’entità della diffusione del cancro (Hong & Xu, 2022). La radioterapia viene spesso utilizzata dopo la terapia conservativa del seno o la mastectomia (con fattori di rischio) (Hong & Xu, 2022). La chemioterapia sistemica può essere somministrata prima o dopo l'intervento chirurgico, a seconda delle circostanze specifiche (Hong & Xu, 2022). I carcinomi mammari positivi ai recettori ormonali possono essere trattati con farmaci che bloccano gli effetti degli estrogeni e del progesterone. L’immunoterapia è un’opzione terapeutica emergente per alcuni carcinomi mammari, poiché aiuta il sistema immunitario a riconoscere e attaccare le cellule tumorali (Hong & Xu, 2022).

Tecniche di imaging

Mammografia digitale

La mammografia digitale è la tecnica più comunemente utilizzata per lo screening del carcinoma mammario. Si tratta di una tecnica di proiezione bidimensionale in cui i raggi X emessi da un tubo radiogeno vengono assorbiti in varia misura dai tessuti e misurati da un rilevatore situato all'altra estremità. Nelle immagini risultanti, i tessuti più densi appaiono più luminosi rispetto ai tessuti meno densi. Durante l'acquisizione dell'immagine i seni vengono compressi per distribuire il tessuto mammario su una superficie più ampia (Ikeda, 2011a). Ciò riduce la sovrapposizione tra le diverse componenti del tessuto mammario, diminuisce la dispersione dei raggi X in transito e migliora il contrasto. Di solito vengono acquisite due proiezioni di ciascun seno: cranio-caudale (CC) e medio-laterale (MLO) (Ikeda, 2011a).

La mammografia digitale è una tecnica veloce e utile per lo screening del carcinoma mammario, ma presenta alcuni inconvenienti (Ikeda, 2011a). La compressione del seno può essere dolorosa e la sovrapposizione di tessuti diversi nonostante la compressione spesso porta ad artefatti (Ikeda, 2011a). Il quadrante superiore interno del seno, che è meno mobile poiché fissato alla parete toracica, è particolarmente difficile da visualizzare alla mammografia (Ikeda, 2011a). Il carcinoma può inoltre essere molto difficile da individuare alla mammografia nel caso dei seni con una grande percentuale di tessuto denso (Ikeda, 2011a).

Tomosintesi mammaria digitale

La tomosintesi mammaria digitale (Digital Breast Tomosynthesis, DBT) prevede l'acquisizione di immagini utilizzando una sorgente di raggi X che si muove lungo un arco di escursione. Le “fette” (sezioni) sottili vengono ricostruite consentendo funzionalità di imaging a 3D destinate a ridurre al minimo l'influenza del tessuto mammario sovrapposto. Questo è particolarmente utile nel caso dell'imaging di lesioni mammarie situate nel parenchima mammario eterogeneo e denso. Uno studio ha rilevato che la DBT è più sensibile per il rilevamento del carcinoma mammario rispetto alla mammografia digitale (Digital Mammography, DM). La DBT può essere combinata con la DM, e uno studio ha mostrato come la combinazione di queste tecniche migliori il rilevamento del carcinoma mammario (Alabousi et al., 2020; Lei et al., 2014; Skaane et al., 2019). È inoltre possibile una combinazione con la mammografia. Tuttavia, la DBT richiede un maggior tempo di acquisizione rispetto alla mammografia e presenta movimento e altri artefatti (Tirada et al., 2019).

Ecografia

Nell'ecografia diagnostica, un trasduttore emette onde sonore ad alta frequenza che attraversano i tessuti, rimbalzando su di essi e creando "echi" che vengono riflessi e rilevati dal trasduttore. Questi echi vengono quindi elaborati per creare immagini in tempo reale su un monitor in base al tempo impiegato dagli echi per raggiungere i tessuti e tornare indietro. Si tratta di una tecnica sicura e relativamente a basso costo che viene spesso utilizzata in aggiunta alla mammografia (Ikeda, 2011b), soprattutto per un’ulteriore valutazione di un reperto rilevato alla palpazione o un reperto mammografico.

Può anche essere utilizzata come modalità di screening principale nelle donne di età inferiore ai 30 anni o nelle donne in gravidanza o in allattamento (Dixon, 2008; Ikeda, 2011b). L'ecografia è molto utile per chiarire se una massa è cistica o solida e quale tipo di margini e vascolarizzazione presenta (Dixon, 2008; Ikeda, 2011b). Aiuta altresì a rilevare altre masse e linfonodi ascellari sospetti (Dixon, 2008; Ikeda, 2011b). Il suo principale svantaggio è che la qualità dell'esame dipende fortemente dall'operatore (Dixon, 2008; Ikeda, 2011b).

Risonanza magnetica per immagini

La risonanza magnetica per immagini (RMI) utilizza un potente campo magnetico e una serie di onde a radiofrequenza per perturbare i nuclei di idrogeno nei tessuti e creare così immagini dettagliate del corpo in sezione trasversale (Daniel & Ikeda, 2011; Mann et al., 2019). Poiché i tessuti con composizioni diverse rispondono a questa perturbazione in modi diversi, la RMI può rilevare molto bene anche sottili differenze tra i tipi di tessuti molli ed è considerata la modalità più sensibile per la diagnosi del carcinoma mammario (Daniel & Ikeda, 2011; Mann et al., 2019). Viene utilizzata principalmente per lo screening di pazienti ad alto rischio in base a fattori di rischio genetici o acquisiti (Daniel & Ikeda, 2011).

La RMI della mammella richiede bobine mammarie dedicate che trasmettono le onde a radiofrequenza e ricevono il segnale generato. Le immagini vengono spesso acquisite con una risoluzione spaziale nel piano di 1 mm, uno spessore della sezione inferiore a 3 mm e la soppressione del segnale del tessuto adiposo. Le sequenze comunemente utilizzate includono immagini pesate in T2, immagini pesate in diffusione e RMI dinamica con mezzo di contrasto. Per ridurre i falsi positivi dovuti a cambiamenti aspecifici del parenchima mammario, è preferibile eseguire la RMI tra il 7º e il 13º giorno del ciclo mestruale (Daniel & Ikeda, 2011).

A differenza della mammografia, la RMI non prevede l’uso di radiazioni ionizzanti e produce immagini tridimensionali che facilitano il rilevamento di lesioni molto piccole (DeMartini & Lehman, 2008; Shahid et al., 2016). La RMI consente inoltre una valutazione più dettagliata della parete toracica rispetto alla mammografia e all’ecografia (DeMartini & Lehman, 2008). Gli svantaggi della RMI mammaria includono una bassa sensibilità alle microcalcificazioni, il costo elevato e la controindicazione nelle persone con determinati impianti metallici (Daniel & Ikeda, 2011).

Screening e problemi di diagnostica

Nonostante le evidenze a sostegno del beneficio complessivo dello screening del carcinoma mammario (Dibden et al., 2020; Kalager et al., 2010; Tabár et al., 2019), sono diverse le problematiche tecniche e logistiche che si trova ad affrontare. Più della metà delle donne sottoposte a screening annuale per 10 anni presenterà un test falso positivo (Hubbard et al., 2011). Ciò ha conseguenze significative e di ampia portata, tra cui il carico fisico ed emotivo di biopsie non necessarie e l’aumento della spesa sanitaria (Nelson, Pappas, et al., 2016; Ong & Mandl, 2015). Inoltre, spesso lo screening non rileva il carcinoma mammario, in particolare nelle donne con seni densi (Banks et al., 2006).

Lo screening del carcinoma mammario richiede operatori altamente qualificati, tra cui radiologi e tecnici di radiologia, di cui attualmente vi è una carenza a livello globale (Moran & Warren-Forward, 2012; Rimmer, 2017; Wing & Langelier, 2009). Questo problema è aggravato dal fatto che lo standard di cura nello screening mammografico in molti Paesi europei prevede che ogni esame venga letto da due radiologi in modo indipendente (Giordano et al., 2012) e dal fatto che, in alcuni Paesi come gli Stati Uniti, gli ostacoli per l’ottenimento della qualifica per l’interpretazione delle mammografie sono elevati a causa dei rigorosi standard di certificazione professionale (Food and Drug Administration, 2001).

Esistono anche notevoli ostacoli alla diffusione dello screening del carcinoma mammario in tutto il mondo. Questi includono la mancanza o la difficoltà di accesso ai programmi di screening, la mancanza di conoscenza o l’incomprensione dei benefici di questi programmi, nonché barriere sociali e culturali (Mascara & Constantinou, 2021).

Il ruolo dell'intelligenza artificiale (IA)

Miglioramenti tecnici

Pochi studi pubblicati hanno studiato finora direttamente l’uso dell’IA per apportare miglioramenti tecnici agli esami senologici. Un’applicazione disponibile in commercio fornisce ai tecnici di radiologia feedback in tempo reale sull’adeguatezza del posizionamento del paziente per le mammografie. (Volpara Health, 2022). Altre applicazioni di IA si sono concentrate sulla riduzione delle dosi di radiazioni (J. Liu et al., 2018), sul miglioramento della ricostruzione delle immagini (Kim et al., 2016) e sulla riduzione del rumore e degli artefatti alla DBT (Garrett et al., 2018).

La DBT è spesso combinata con la mammografia digitale per lo screening del carcinoma mammario, con il conseguente raddoppiamento della dose di radiazioni ricevuta dal paziente (Svahn et al., 2015). Per evitare ciò, è stato rivolto un crescente interesse alla generazione di mammografie sintetiche a partire dai dati della DBT (Chikarmane et al., 2023). In un ampio studio prospettico norvegese, l’accuratezza della DBT combinata con la mammografia digitale o la mammografia sintetica per il rilevamento del carcinoma mammario è risultata molto simile (Skaane et al., 2019). Studi recenti hanno valutato il miglioramento della qualità della mammografia sintetica mediante l’IA con risultati promettenti (Balleyguier et al., 2017; James et al., 2018).

Miglioramenti diagnostici

Valutazione della densità del seno

Il tessuto mammario denso visibile alla mammografia costituisce il tessuto fibroghiandolare. Le donne con seni densi presentano un rischio da 2 a 4 volte maggiore di soffrire di carcinoma mammario rispetto alle donne con seni con tessuto mammario più adiposo (Byrne et al., 1995; Duffy et al., 2018; Torres-Mejía et al., 2005). Inoltre, la sensibilità della mammografia per il carcinoma mammario è inferiore del 20-30% nei seni densi rispetto a quelli meno densi (Lynge et al., 2019). Lo standard di cura nella valutazione della densità del seno utilizza la classificazione BI-RADS (Berg et al., 2000).

Diversi studi di grande respiro hanno esaminato il potenziale della valutazione automatica della densità del seno sulle mammografie utilizzando strumenti basati sull’IA. Una rete neurale convoluzionale (Convolutional Neural Network, CNN) addestrata con 14.000 mammografie e testata su quasi 2.000 mammografie ha classificato la densità del seno come “densità fibroghiandolare sparsa” o “densità eterogenea” con un’area sotto la curva (Area Under the Curve, AUC) di 0,93 (Mohamed et al., 2018). Un altro studio ha utilizzato una CNN in grado di effettuare la classificazione BI-RADS sia a due che a quattro categorie addestrata con oltre 40.000 mammografie (Lehman et al., 2019). In un set di dati di test composto da oltre 8.000 mammografie è stato riscontrato un buon accordo sulla densità del seno tra l’algoritmo e i singoli radiologi (kappa = 0,67), nonché il consenso di cinque radiologi (kappa = 0,78) (Lehman et al., 2019).

Rilevamento del carcinoma mammario

In una revisione sistematica di 82 studi in cui è stata utilizzata l'IA per il rilevamento del carcinoma mammario con vari standard di riferimento, gli autori hanno rilevato un'AUC di 0,87 per gli studi che utilizzavano la mammografia, di 0,91 per gli studi che utilizzavano l’ecografia e per quelli che utilizzavano la DBT e di 0,87 per gli studi che utilizzavano la RMI (Aggarwal et al., 2021). Si tratta di risultati promettenti, tuttavia i confronti diretti tra gli algoritmi basati sull’IA e i radiologi presentano margini di miglioramento. In un'altra revisione sistematica di studi con l'istopatologia o il follow-up (per le donne negative allo screening) come riferimento, il 94% delle 36 CNN identificate sono risultate meno accurate di un singolo radiologo e tutte sono risultate meno accurate del consenso di 2 o più radiologi se utilizzate come sistema indipendente (Freeman et al., 2021). Le evidenze attuali, dunque, non supportano l’uso dell’IA come strategia indipendente per il rilevamento del carcinoma mammario.

Previsione del carcinoma mammario

L’IA si è dimostrata promettente nel predire il rischio di sviluppare il carcinoma mammario sulla base delle mammografie di screening, fornendo una migliore valutazione della densità del seno, che costituisce un fattore di rischio accertato per il carcinoma mammario (Duffy et al., 2018), oppure rilevando sottili caratteristiche dell’imaging che sono foriere di cancro (Batchu et al., 2021). Diversi studi hanno utilizzato modelli basati sull’IA per prevedere il rischio di sviluppare il carcinoma mammario in futuro sulla base delle mammografie (Batchu et al., 2021; Geras et al., 2019).

Una CNN addestrata con quasi 1.000.000 di immagini mammografiche ha mostrato un’AUC di 0,65 per la previsione dello sviluppo futuro del carcinoma mammario rispetto a un valore di 0,57-0,60 per i punteggi di densità del seno basati sulla mammografia convenzionale (Dembrower, Liu, et al., 2020). Uno studio più piccolo ha rilevato un’AUC di 0,73 per un metodo basato su CNN per la previsione del carcinoma mammario sulla base di normali immagini mammografiche. (Arefan et al., 2020). Un altro algoritmo di deep learning ha mostrato un’AUC di 0,82 per la previsione dei cancri intervallo (tumori rilevati entro 12 mesi dopo una mammografia negativa) rispetto a un valore di 0,65 per la valutazione visiva BIRADS della densità del seno (Hinton et al., 2019). Un altro modello basato sul deep learning che incorporava sia fattori di rischio che reperti mammografici per prevedere il rischio di carcinoma mammario presentava un’AUC fino a 0,7, superando l’accuratezza dei modelli predittivi basati solo sui fattori di rischio o sui reperti mammografici. (Yala, Lehman, et al., 2019).

Miglioramenti dell'efficienza

L’enorme volume di esami mammografici e la carenza di radiologi qualificati hanno reso il miglioramento dell’efficienza una delle aree di ricerca più interessanti sull’uso dell’IA nel carcinoma mammario.

In uno studio, gli autori hanno simulato un flusso di lavoro in cui le mammografie venivano interpretate da un radiologo e da un modello di deep learning e la decisione veniva considerata definitiva in caso di concordanza (McKinney et al., 2020). È stato consultato un secondo radiologo solo in caso di disaccordo, con una conseguente riduzione del carico di lavoro dell’88% per il secondo radiologo con un valore predittivo negativo di oltre il 99,9% (McKinney et al., 2020).

In un ampio studio clinico randomizzato, primo nel suo genere, condotto in Svezia, circa 80.000 donne sono state assegnate alla lettura o meno delle loro mammografie di screening da parte di una CNN (Lång et al., 2023). Nel braccio di intervento, sono state sottoposte a doppia lettura solo le mammografie a cui era stato assegnato un punteggio corrispondente a un’elevata probabilità di malignità (il resto è stato letto da un solo radiologo) e i risultati sono stati confrontati con la doppia lettura convenzionale senza l’aiuto dell’algoritmo. In un'analisi ad interim dei dati di 80.000 donne, entrambi i bracci dello studio hanno mostrato un identico tasso di falsi positivi, par all'1,5%. Il valore predittivo positivo del richiamo è stato del 28,3% nel gruppo di intervento e del 24,8% nel gruppo di controllo, e la strategia ha ridotto il carico di lavoro del 44,3% (Lång et al., 2023).

Altri studi hanno utilizzato l’IA per effettuare uno screening preliminare delle mammografie, selezionando quelle con una bassa probabilità di cancro e mostrando a un radiologo solo quelle con un’alta probabilità. Uno studio statunitense ha utilizzato un flusso di lavoro simulato che coinvolgeva una CNN addestrata con oltre 212.000 mammografie e testata su oltre 26.000 a questo scopo (Yala, Schuster, et al., 2019). Il flusso di lavoro che utilizzava l’algoritmo ha mostrato una sensibilità non inferiore per il carcinoma mammario (90,1% vs. 90,6%) e una specificità leggermente maggiore (94,2% vs. 93,5%) rispetto ai radiologi che lavoravano da soli ed è risultato associato a un carico di lavoro inferiore del 19,3% (Yala, Schuster, et al., 2019). Uno studio più piccolo condotto in Spagna ha rilevato una riduzione del carico di lavoro del 72,5% grazie all’utilizzo dell'IA per il triage solo dei casi di DBT ad alto rischio per la lettura da parte di un secondo radiologo e del 29,7% grazie all’utilizzo dell'IA per il triage solo degli studi di DBT ad alto rischio per la lettura da parte di un secondo radiologo, rispetto alla tradizionale doppia lettura dei flussi di lavoro mammografici (Raya-Povedano et al., 2021). È stata inoltre riscontrata una sensibilità non inferiore di questa strategia di utilizzo dell’IA per il triage dei casi di mammografia e DBT ad alto rischio per una seconda lettura rispetto alla doppia lettura standard dei flussi di lavoro mammografici e di DBT (Raya-Povedano et al., 2021). In uno studio svedese, una strategia simile che si avvaleva di un algoritmo di IA disponibile in commercio ha prodotto un tasso di falsi negativi non superiore al 4% e l’algoritmo ha dimostrato di essere in grado di rilevare potenzialmente ulteriori 71 tumori in più ogni 1.000 esami rispetto a una doppia lettura negativa da parte di radiologi umani in pazienti ritenuti ad altissimo rischio dall'algoritmo di IA (Dembrower, Wåhlin, et al., 2020).

In uno studio su oltre un milione di mammografie in otto centri di screening e con dispositivi di tre diversi produttori, un algoritmo di deep learning disponibile in commercio ha determinato nel 63% dei casi che non fossero necessari ulteriori esami sulla base di valutazioni degli esami ad alta affidabilità (Leibig et al., 2022). Il resto degli esami, per i quali la sicurezza dell'algoritmo era bassa, sono stati mostrati ai radiologi. Questa strategia ha migliorato la sensibilità dei radiologi (rispetto alla lettura senza ausilio) del 2,6-4% e la specificità dello 0,5-1,0% (Leibig et al., 2022).

Problemi e orientamenti futuri

Diverse problematiche etiche, tecniche e metodologiche associate all’uso dell’IA nello screening del carcinoma mammario forniscono un quadro per guidare la ricerca futura in questo campo (Hickman et al., 2021).

La maggior parte degli strumenti basati sull'IA si sono finora concentrati sulla mammografia digitale (Aggarwal et al., 2021), ma altre tecniche di esame come la DBT e la RMI presentano vantaggi unici (Alsheik et al., 2019; Mann et al., 2019) e probabilmente ricopriranno in futuro un ruolo più importante nello screening del carcinoma mammario. Tuttavia, poiché la DBT e la RMI sono tecniche tomografiche che producono immagini tridimensionali, la loro elaborazione mediante strumenti basati sull’IA richiederà uno spazio di archiviazione e una potenza di calcolo maggiori (Prevedello et al., 2019).

L’incidenza, la presentazione e l’esito del carcinoma mammario sono correlati a diversi fattori sociodemografici, tra cui razza ed etnia (Hirko et al., 2022; Hu et al., 2019; Martini et al., 2022). L’addestramento di strumenti basati sull’IA con set di dati rappresentativi di una popolazione diversificata è fondamentale per garantire che possano essere generalizzati e arrecare benefici al maggior numero di persone possibile.

Le prestazioni complessive dell’IA nel rilevamento del carcinoma mammario sono state ragguardevoli. Tuttavia, è interessante notare che in uno studio non è stato possibile dimostrare la non inferiorità della sensibilità dell’IA nel rilevamento del carcinoma mammario rispetto a quella dei radiologi (Lauritzen et al., 2022). Inoltre, si nutrono preoccupazioni circa la qualità delle evidenze alla base di molti studi su questo argomento. Una revisione sistematica che ha studiato l’accuratezza degli strumenti basati sull’IA nel rilevamento del carcinoma mammario ha identificato diverse aree di potenziale miglioramento (Freeman et al., 2021). La revisione non ha individuato studi prospettici e gli studi identificati erano di scarsa qualità metodologica. In particolare, gli autorihanno osservato che gli studi più piccoli hanno mostrato risultati più positivi che non sono stati riprodotti in studi di più ampio respiro. In un’altra revisione sistematica, solo circa un decimo degli studi aveva utilizzato un set di dati esterno per la convalida, nessuno studio aveva fornito un calcolo prespecificato delle dimensioni del campione e sono stati identificati seri problemi con bias di selezione e standard di riferimento inappropriati (Aggarwal et al., 2021). Questi problemi metodologici possono potenzialmente essere mitigati in futuro con l’introduzione di grandi archivi di dati aperti (Nguyen et al., 2023) e una maggiore aderenza alle linee guida per la conduzione di ricerche mediche basate sull'IA (Lekadir et al., 2021; X. Liu et al., 2020).

Conclusioni

L’integrazione dell’intelligenza artificiale nei programmi di screening del carcinoma mammario è promettente nel migliorare la qualità delle immagini e l'efficienza e nel prevedere il rischio futuro di carcinoma mammario. Per il rilevamento del carcinoma mammario negli esami di screening, le evidenze disponibili suggeriscono che l’intelligenza artificiale presenta risultati migliori quando viene usata in sinergia con i radiologi. Sono fondamentali ricerche continue per affrontare le problematiche associate all'uso dell'IA nello screening del carcinoma mammario, tra cui l'espansione del suo campo di applicazione oltre la mammografia e la garanzia di un uso etico e responsabile. Con la continua evoluzione delle applicazioni di IA, il futuro dello screening del carcinoma mammario racchiude un immenso potenziale per una maggiore accessibilità, un intervento precoce e, in definitiva, migliori risultati per i/le pazienti.

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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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