Clinical actionability of triaging DNA mismatch repair deficient colorectal cancer from biopsy samples using deep learning.

BACKGROUND: We aimed to develop a deep learning (DL) model to predict DNA mismatch repair (MMR) status in colorectal cancers (CRC) based on hematoxylin and eosin-stained whole-slide images (WSIs) and assess its clinical applicability. METHODS: The DL model was developed and validated through three-fold cross validation using 441 WSIs from the Cancer Genome Atlas (TCGA) and externally validated using 78 WSIs from the Pathology AI Platform (PAIP), and 355 WSIs from surgical specimens and 341 WSIs from biopsy specimens of the Sun Yet-sun University Cancer Center (SYSUCC). Domain adaption and multiple instance learning (MIL) techniques were adopted for model development. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC). A dual-threshold strategy was also built from the surgical cohorts and validated in the biopsy cohort. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and the percentage of patients avoiding IHC testing were evaluated. FINDINGS: The MIL model achieved an AUROC of 0·8888±0·0357 in the TCGA-validation cohort, 0·8806±0·0232 in the PAIP cohort, 0·8457±0·0233 in the SYSUCC-surgical cohort, and 0·7679±0·0342 in the SYSUCC-biopsy cohort. A dual-threshold triage strategy was used to rule-in and rule-out dMMR patients with remaining uncertain patients recommended for further IHC testing, which kept sensitivity higher than 90% and specificity higher than 95% on deficient MMR patient triage from both the surgical and biopsy specimens, result in more than half of patients avoiding IHC based MMR testing. INTERPRETATION: A DL-based method that could directly predict CRC MMR status from WSIs was successfully developed, and a dual-threshold triage strategy was established to minimize the number of patients for further IHC testing. FUNDING: The study was funded by the National Natural Science Foundation of China (82073159, 81871971 and 81700576), the Natural Science Foundation of Guangdong Province (No. 2021A1515011792 and No.2022A1515012403) and Medical Scientific Research Foundation of Guangdong Province of China (No. A2020392).

A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer.

BACKGROUND: Tumor-infiltrating lymphocytes (TILs) are clinically significant in triple-negative breast cancer (TNBC). Although a standardized methodology for visual TILs assessment (VTA) exists, it has several inherent limitations. We established a deep learning-based computational TIL assessment (CTA) method broadly following VTA guideline and compared it with VTA for TNBC to determine the prognostic value of the CTA and a reasonable CTA workflow for clinical practice. METHODS: We trained three deep neural networks for nuclei segmentation, nuclei classification and necrosis classification to establish a CTA workflow. The automatic TIL (aTIL) score generated was compared with manual TIL (mTIL) scores provided by three pathologists in an Asian (n = 184) and a Caucasian (n = 117) TNBC cohort to evaluate scoring concordance and prognostic value. FINDINGS: The intraclass correlations (ICCs) between aTILs and mTILs varied from 0.40 to 0.70 in two cohorts. Multivariate Cox proportional hazards analysis revealed that the aTIL score was associated with disease free survival (DFS) in both cohorts, as either a continuous [hazard ratio (HR)=0.96, 95% CI 0.94-0.99] or dichotomous variable (HR=0.29, 95% CI 0.12-0.72). A higher C-index was observed in a composite mTIL/aTIL three-tier stratification model than in the dichotomous model, using either mTILs or aTILs alone. INTERPRETATION: The current study provides a useful tool for stromal TIL assessment and prognosis evaluation for patients with TNBC. A workflow integrating both VTA and CTA may aid pathologists in performing risk management and decision-making tasks. FUNDING: National Natural Science Foundation of China, Guangdong Medical Research Foundation, Guangdong Natural Science Foundation.

Use of eye care services among diabetic patients in urban and rural China.

OBJECTIVE: To assess the use of eye care and its predictors among diabetic patients in China. DESIGN: Cross-sectional, clinic-based study. PARTICIPANTS: Diabetic patients 18 years of age or older were recruited consecutively from an urban tertiary and community hospitals and from a rural clinic in Guangdong, China. METHODS: Information obtained by questionnaire and chart review included: demographic and socioeconomic status, knowledge about diabetic retinopathy (DR), and ocular and medical history. MAIN OUTCOME MEASURES: Self-reported or chart history of an eye examination ever or within the preceding 12 months. RESULTS: The participation rate among 889 eligible subjects was 92.7%. Among 824 participants (mean age, 62.6+/-12.9 years; 58.8% female), 550 (66.7%) had not been examined in the last year as recommended by the American Academy of Ophthalmology, and 356 (43.2%) had never been examined. For the rural hospital, these figures were 81.1% and 68.7%, respectively. In regression analyses, factors associated with having an eye examination in the last year were: attendance at urban hospitals (odds ratio [OR], 3.46 [P<0.001] and 1.76 [P = 0.021] for the tertiary and community hospitals, respectively, compared with the rural clinic), higher DR knowledge score (OR, 1.24; P = 0.001), greater concern about vision loss (OR, 1.22; P = 0.007), and recommendation of regular eye examinations by the provider (OR, 2.36; P = 0.011). Predictors of ever having an eye examination were similar. Monthly income and health insurance status were not predictive of being examined. CONCLUSIONS: These results suggest that the low proportion of diabetic receiving recommended annual eye examinations in China may be improved through patient and physician education.