Interpretable Deep Learning Predicts the Molecular Endometrial Cancer Classification from H&amp;E Images: A Combined Analysis of the Portec Randomized Clinical Trials

Fremond, Sarah; Andani, Sonali; Wolf, Jurriaan Barkey; Dijkstra, Jouke; Melsbach, Sinead; Jobsen, Jan J.; Brinkhuis, Mariël; Roothaan, Suzan; Jürgenliemk-Schulz, Ina M.; Lutgens, Ludy; Nout, Remi A.; Steen-Banasik, Elzbieta M. van der; Boer, Stephanie M. de; Powell, Melanie; Singh, Naveena; Mileshkin, Linda R.; Mackay, Helen; Léary, Alexandra; Nijman, Hans W.; Creutzberg, Carien L.; Horeweg, Nanda; Koelzer, Viktor H.; Bosse, Tjalling

doi:10.2139/ssrn.4144537

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…A publicly available dataset with endometrial cancer H&E slides on which artificial intelligence papers are published is the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [10] available from the cancer imaging archive, consisting of pathology slides along with genomic data and radiology images. The three studies that used CPTAC aimed to predict the same information as genetic sequencing [11] or illustrate features in H&E slides that could identify different cancer variants [12,13] and hence allow more personalised treatment. A paper by Song et al [14] uses a dataset from the cancer genome atlas along with the CPTAC dataset to distinguish between subtypes of endonmetrial cancer.…”

Section: Introductionmentioning

confidence: 99%

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

et al. 2023

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In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either “malignant”, “other or benign” or “insufficient”. An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with “malignant” and “other or benign” areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being “malignant” or “other or benign”. Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either “malignant”, “other or benign” or “insufficient”. The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists’ workload.

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Section: Introductionmentioning

confidence: 99%

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

et al. 2023

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“…At the current stage of our research, AI classification of GRC levels does not provide a precise criterion for which the level of drug resistance affects drug effectiveness, or which alternative drugs should be considered at each drug resistance level. To date, most studies have addressed either genetics or image-based analysis, and only a few studies have integrated both approaches (Couture et al, 2018;Wulczyn et al, 2020;Ash et al, 2021;Schneider et al, 2022;Fremond et al, 2023). These combined therapeutic strategies with the integration of genetics hold prognostic potential, providing reference basis for drug choices.…”

Section: Accuracy Sensitivity Specificitymentioning

confidence: 99%

Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis

Tak,

Han,

Leem

et al. 2024

Front. Bioeng. Biotechnol.

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Bladder cancer is the most common urological malignancy worldwide, and its high recurrence rate leads to poor survival outcomes. The effect of anticancer drug treatment varies significantly depending on individual patients and the extent of drug resistance. In this study, we developed a validation system based on an organ-on-a-chip integrated with artificial intelligence technologies to predict resistance to anticancer drugs in bladder cancer. As a proof-of-concept, we utilized the gemcitabine-resistant bladder cancer cell line T24 with four distinct levels of drug resistance (parental, early, intermediate, and late). These cells were co-cultured with endothelial cells in a 3D microfluidic chip. A dataset comprising 2,674 cell images from the chips was analyzed using a convolutional neural network (CNN) to distinguish the extent of drug resistance among the four cell groups. The CNN achieved 95.2% accuracy upon employing data augmentation and a step decay learning rate with an initial value of 0.001. The average diagnostic sensitivity and specificity were 90.5% and 96.8%, respectively, and all area under the curve (AUC) values were over 0.988. Our proposed method demonstrated excellent performance in accurately identifying the extent of drug resistance, which can assist in the prediction of drug responses and in determining the appropriate treatment for bladder cancer patients.

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Interpretable Deep Learning Predicts the Molecular Endometrial Cancer Classification from H&E Images: A Combined Analysis of the Portec Randomized Clinical Trials

Cited by 2 publications

References 41 publications

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis

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