Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer

Abstract

PURPOSE To develop an artificial intelligence (AI)–based model for identifying patients with lymph node (LN) metastasis based on digital evaluation of primary tumors and train the model using cystectomy specimens available from The Cancer Genome Atlas (TCGA) Project; patients from our institution were included for validation of the leave-out test cohort. METHODS In all, 307 patients were identified for inclusion in the study (TCGA, n = 294; in-house, n = 13). Deep learning models were trained from image patches at 2.5×, 5×, 10×, and 20× magnifications, and spatially resolved prediction maps were combined with microenvironment (lymphocyte infiltration) features to derive a final patient-level AI score (probability of LN metastasis). Training and validation included 219 patients (training, n = 146; validation, n = 73); 89 patients (TCGA, n = 75; in-house, n = 13) were reserved as an independent testing set. Multivariable logistic regression models for predicting LN status based on clinicopathologic features alone and a combined model with AI score were fit to training and validation sets. RESULTS Several patients were determined to have positive LN metastasis in TCGA (n = 105; 35.7%) and in-house (n = 3; 23.1%) cohorts. A clinicopathologic model that considered using factors such as age, T stage, and lymphovascular invasion demonstrated an area under the curve (AUC) of 0.755 (95% CI, 0.680 to 0.831) in the training and validation cohorts compared with the cross validation of the AI score (likelihood of positive LNs), which achieved an AUC of 0.866 (95% CI, 0.812 to 0.920; P = .021). Performance in the test cohort was similar, with a clinicopathologic model AUC of 0.678 (95% CI, 0.554 to 0.802) and an AI score of 0.784 (95% CI, 0.702 to 0.896; P = .21). In addition, the AI score remained significant after adjusting for clinicopathologic variables ( P = 1.08 × 10−9), and the combined model significantly outperformed clinicopathologic features alone in the test cohort with an AUC of 0.807 (95% CI, 0.702 to 0.912; P = .047). CONCLUSION Patients who are at higher risk of having positive LNs during cystectomy can be identified on primary tumor samples using novel AI-based methodologies applied to digital hematoxylin and eosin–stained slides.

Identification and Validation of the Prognostic Stemness Biomarkers in Bladder Cancer Bone Metastasis

“…The construction of prediction model can assist oncologists in clinical decisionmarking, thus many previous studies have focused on the identification of prognostic biomarkers in patients with BLCA (29)(30)(31). A lot of statistical methods, such as deep learning, Cox regression and LASSO regression analysis, have been used in the identification of the prognostic factors including the clinical information (age, clinical stage, and lymphovascular invasion), laboratory examination (C-reactive protein); molecular features (competing endogenous RNA, immune infiltration) (29,30,(32)(33)(34). Although these results revealed the feasibility of personalized risk factors identification in predicting the prognosis of BLCA, none of them included the CSC-related signatures and PSRGs.…”

Identification of the prognostic Enhancer RNA in bladder cancer bone metastasis

“…In clinic, the prediction model may help oncologists to make therapeutic strategy. Therefore, many previous studies have explored the biomarkers and predict the prognosis of patients with BLCA [24][25][26] . In these prediction models, various statistical methods, such as deep learning, Cox regression and LASSO regression analysis, have been used and many prognostic factors have been included, such as clinical information (age, clinical stage and metastasis), laboratory examination (in ammatory indicators); molecular features (competing endogenous RNA, immune in ltration) 24,25,27−29 .…”

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

“…( digital pathology [Title]) AND ( artificial intelligence [Title]) currently reports 17 works [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

“… In a Special Issue [ 14 ] and in an opinion article [ 24 ] of a working group in pathological diagnostics in toxicology. Through a report [ 22 ] for the prediction of positive lymph nodes from primary tumors in bladder cancer. In cancer staging [ 18 ], it is well known that recent AI approaches have been applied to pathology images toward diagnostic, prognostic, and treatment prediction-related tasks in cancer.…”