xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer
Por:
Bustos, A, Paya, A, Torrubia, A, Jover, R, Llor, X, Bessa, X, Castells, A, Carracedo, A and Alenda, C
Publicada:
1 dic 2021
Ahead of Print:
29 nov 2021
Resumen:
The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. Nonetheless, batch effects or systematic biases are not well characterized in digital histology models and lead to overoptimistic estimates of model performance. Methods to not only palliate but to directly abrogate biases are needed. We present a multiple bias rejecting DL system based on adversarial networks for the prediction of MSI in CRC from tissue microarrays (TMAs), trained and validated in 1788 patients from EPICOLON and HGUA. The system consists of an end-to-end image preprocessing module that tile samples at multiple magnifications and a tissue classification module linked to the bias-rejecting MSI predictor. We detected three biases associated with the learned representations of a baseline model: the project of origin of samples, the patient's spot and the TMA glass where each spot was placed. The system was trained to directly avoid learning the batch effects of those variables. The learned features from the bias-ablated model achieved maximum discriminative power with respect to the task and minimal statistical mean dependence with the biases. The impact of different magnifications, types of tissues and the model performance at tile vs patient level is analyzed. The AUC at tile level, and including all three selected tissues (tumor epithelium, mucin and lymphocytic regions) and 4 magnifications, was 0.87 +/- 0.03 and increased to 0.9 +/- 0.03 at patient level. To the best of our knowledge, this is the first work that incorporates a multiple bias ablation technique at the DL architecture in digital pathology, and the first using TMAs for the MSI prediction task.
Filiaciones:
Bustos, A:
MedBravo, AI Canc Res Unit, Alicante 03560, Spain
:
Alicante Univ Gen Hosp HGUA, Pathol Dept, Alicante 03010, Spain
Alicante Inst Hlth & Biomed Res ISABIAL, Alicante 03010, Spain
Torrubia, A:
MedBravo, AI Canc Res Unit, Alicante 03560, Spain
:
Alicante Univ Gen Hosp HGUA, Pathol Dept, Alicante 03010, Spain
Alicante Inst Hlth & Biomed Res ISABIAL, Alicante 03010, Spain
Llor, X:
Yale Univ, Dept Med, Yale Canc Ctr, New Haven, CT 06511 USA
Bessa, X:
Hosp del Mar Med Res Inst IMIM, Gastroenterol Dept, Barcelona 08003, Spain
Castells, A:
Univ Barcelona, Inst Invest Biomed August Pi & Sunyer IDIBAPS, Ctr Invest Biomed Red Enfermedades Hepat & Digest, Gastroenterol Dept,Hosp Clin, Barcelona 08036, Spain
Carracedo, A:
Fdn Publ Gallega Med Genom GMX, Santiago De Compostela 15706, Spain
:
Alicante Univ Gen Hosp HGUA, Pathol Dept, Alicante 03010, Spain
Alicante Inst Hlth & Biomed Res ISABIAL, Alicante 03010, Spain
Green Published, Green Submitted
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