Ms. Aya Aqeel Profile

Aya Aqeel

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Publications (1)

Proceedings Article | 4 April 2022 Presentation + Paper

Computer extracted features of tumor-infiltrating lymphocytes (TILs) architecture are prognostic of progression-free survival in stage III colon cancer

Aya Aqeel, Germán Corredor, Vidya Sankar Viswanathan, Chuheng Chen, Mogjan Mokhtari, Pingfu Fu, Joseph Willis, Anant Madabhushi

Proceedings Volume 12039, 120390V (2022) https://doi.org/10.1117/12.2613161

KEYWORDS: Colorectal cancer, Tumors, Feature extraction, Cancer, Image segmentation, Data modeling, Medical research

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Stage III Colorectal cancer (CRC) is treated with surgery followed by chemotherapy. Yet >20% of clinically low-risk patients develop recurrence. It is critical to identify high-risk stage III patients who can benefit from closer monitoring and escalation of therapy. Previous studies showed promising results in predicting risk from H&E slides in CRC using deep learning based algorithms. One biomarker which was heavily studied and showed good results in predicting risk is Tumor Infiltrating Lymphocytes (TILs). In CRC, TIL density has been shown to be significantly and independently associated with overall patient survival¹. Furthermore, additional studies have demonstrated that analyzing spatial organization of TILs could be more informative than density alone². Hence, our study aimed to stratify stage III CRC patients into distinct risk groups based on features derived from TILs and to determine if this classification could have independent prognostic significance. The training set (D₁) included 50 patients and validation set (D₂) consisted of 70 patients from an independent site. A survival model was trained to predict the risk of recurrence in stage III CRC patients. First, a deep learning (DL) model was used to segment TILs on WSIs. Next, 1036 features related to spatial architecture (SpaTIL) and density of TILs (DenTIL) were extracted. A Cox proportional hazards regression model in conjunction with the least absolute shrinkage and selection operator (Lasso) was used to find top 5 features and the feature coefficients associated with progression free survival (PFS) and provide risk scores to each patient. The risk scores for the training dataset were computed using the selected features with their respective coefficients. A cut-off value was determined according to these risk scores, above which patients were labelled high-risk and below was low risk. In the validation set, the median PFS for the high-risk group was 15.1mos and in the low-risk group was 27mos. The model was able to accurately predict higher incidence of progression in patients in the high-risk group (HR = 3.76, 95% CI 1.3-10.9, p-value=0.0053, c-index=0.687) in the validation set. Future work will entail additional multi-site, multi-institutional validation of our biomarker to further understand its strengths and applications.

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