Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer

Lorenzo Nibid, Carlo Greco, Ermanno Cordelli, Giovanna Sabarese, Michele Fiore, Charles Z Liu, Edy Ippolito, Rosa Sicilia, Marianna Miele, Matteo Tortora, Chiara Taffon, Mehrdad Rakaee, Paolo Soda, Sara Ramella and Giuseppe Perrone

PLOS ONE, 2023, vol. 18, issue 11, 1-24

Abstract: Despite the advantages offered by personalized treatments, there is presently no way to predict response to chemoradiotherapy in patients with non-small cell lung cancer (NSCLC). In this exploratory study, we investigated the application of deep learning techniques to histological tissue slides (deep pathomics), with the aim of predicting the response to therapy in stage III NSCLC. We evaluated 35 digitalized tissue slides (biopsies or surgical specimens) obtained from patients with stage IIIA or IIIB NSCLC. Patients were classified as responders (12/35, 34.7%) or non-responders (23/35, 65.7%) based on the target volume reduction shown on weekly CT scans performed during chemoradiation treatment. Digital tissue slides were tested by five pre-trained convolutional neural networks (CNNs)—AlexNet, VGG, MobileNet, GoogLeNet, and ResNet—using a leave-two patient-out cross validation approach, and we evaluated the networks’ performances. GoogLeNet was globally found to be the best CNN, correctly classifying 8/12 responders and 10/11 non-responders. Moreover, Deep-Pathomics was found to be highly specific (TNr: 90.1) and quite sensitive (TPr: 0.75). Our data showed that AI could surpass the capabilities of all presently available diagnostic systems, supplying additional information beyond that currently obtainable in clinical practice. The ability to predict a patient’s response to treatment could guide the development of new and more effective therapeutic AI-based approaches and could therefore be considered an effective and innovative step forward in personalised medicine.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0294259

DOI: 10.1371/journal.pone.0294259

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