Westerling-Bui, Thomas

Context.— Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation. Objective.— To develop a supportive ecosystem that enables pathologists with variable expertise in artificial intelligence to create algorithms in a development environment with seamless transition to a production environment. Design.— Platform requirements included (1) approachable and intuitive user interface, (2) diverse algorithmic modeling options, (3) support capability for internal and external collaborations, (4) a seamless mechanism for discovery to clinical deployment transition, (5) the ability to meet minimum institutional requirements for information technology (IT) review, and (6) the ability to be scalable over time. The ecosystem required platform education, data science guidance, project management structure, and ongoing leadership. Results.— The development team considered internal development and vended solutions. Because of the extended timeline and resource requirements for internal development, a decision was made to use a vended solution. Vendor proposals were solicited and reviewed by pathologists, IT, and security groups. A vendor was selected and pipelines for development and production were established. Proposals for development were solicited from the pathology department. Eighty-four investigators were selected for the initial cohort, receiving training and access to dedicated subject matter experts. A total of 30 of 31 projects progressed through the model development process of annotating, training, and validation. Based on these projects, 15 abstracts were submitted to national meetings. Conclusions.— Democratizing artificial intelligence by creating an ecosystem to support pathologists with varying levels of expertise can break down entry barriers, reduce overall cost of algorithm development, improve algorithm quality, and enhance the speed of adoption.

Abstract Purpose: There is a need to improve current risk stratification of stage II colorectal cancer to better inform risk of recurrence and guide adjuvant chemotherapy. We sought to examine whether integration of QuantCRC, a digital pathology biomarker utilizing hematoxylin and eosin–stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines. Experimental Design: ASCO and QuantCRC-integrated schemes were applied to a cohort of 398 mismatch-repair proficient (MMRP) stage II colorectal cancers from three large academic medical centers. The ASCO stage II scheme was taken from recent guidelines. The QuantCRC-integrated scheme utilized pT3 versus pT4 and a QuantCRC-derived risk classification. Evaluation of recurrence-free survival (RFS) according to these risk schemes was compared using the log-rank test and HR. Results: Integration of QuantCRC provides improved risk stratification compared with the ASCO scheme for stage II MMRP colorectal cancers. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared with the ASCO scheme (62.5% vs. 42.2%) without compromising excellent 3-year RFS. The QuantCRC-integrated scheme provided larger HR for both intermediate-risk (2.27; 95% CI, 1.32–3.91; P = 0.003) and high-risk (3.27; 95% CI, 1.42–7.55; P = 0.006) groups compared with ASCO intermediate-risk (1.58; 95% CI, 0.87–2.87; P = 0.1) and high-risk (2.24; 95% CI, 1.09–4.62; P = 0.03) groups. The QuantCRC-integrated risk groups remained prognostic in the subgroup of patients that did not receive any adjuvant chemotherapy. Conclusions: Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance for stage II MMRP colorectal cancers.

162 Background: There is a need to improve current risk stratification of stage II and III colorectal cancer (CRC) to better inform risk of recurrence and guide adjuvant chemotherapy. The purpose of this study is to examine whether integration of QuantCRC, an AI-based digital pathology biomarker utilizing hematoxylin and eosin-stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines. Methods: ASCO and QuantCRC-integrated risk schemes were applied to an observational cohort of 1,068 stage II and III CRCs. The stage II integrated scheme utilizes pT3 vs. pT4 and QuantCRC-derived risk groups. The stage III integrated scheme utilizes pT1-3 vs. pT4, pN1 vs. pN2, and QuantCRC-derived risk groups. Performance metrics included log-rank test, hazard ratios and Somers’ Dxy rank correlation. Results: Integration of QuantCRC provides improved risk stratification compared to the ASCO scheme for stage II and III CRC. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared to the ASCO scheme (69.3% vs. 60.4%) without decreased 3-year RFS. The QuantCRC-integrated scheme provided larger hazard ratios (HR) for both intermediate-risk (3.04, 95%CI 1.81-5.10, P=2.8x10-5) and high-risk (4.62, 95%CI 2.02-10.61, P=0.0003) groups compared to ASCO intermediate-risk (2.09, 95%CI 1.21-3.63, P=0.008) and high-risk (3.08, 95%CI 1.57-6.01, P=0.001) groups. The QuantCRC-integrated scheme for stage III tumors identified a small group of 80/518 (15.4%) CRCs at very high risk of recurrence with HR of 4.08 (95%CI 2.68-6.23, P=6.5x10-11) compared to a HR of 2.42 (95%CI 1.72-3.38, P=3.1x10-7) for 228/518 (44.0%) high-risk CRCs in the ASCO scheme. QuantCRC-integrated risk groups remained prognostic in stage III CRCs when stratified by presence or absence of any adjuvant chemotherapy. No difference in RFS were seen in QuantCRC-integrated low-risk and intermediate-risk stage III CRCs stratified by 3 vs. 6-months of oxaliplatin-based adjuvant chemotherapy suggesting that these two groups can be treated with 3-months of adjuvant therapy. Conclusions: Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance.