PQA 07 - PQA 07 Gastrointestinal Cancer and Sarcoma/Cutaneous Tumors Poster Q&A
3056 - Predicting Pathological Lymph Node Involvement Using Machine Learning in Locally Advanced Rectal Cancer Patients Treated with Neoadjuvant Chemo-Radiotherapy
G. Ozden1, S. Gunes2, and M. Adli1; 1Department of Radiation Oncology, Marmara University School of Medicine, Istanbul, Turkey, 2Turkcell Technology Research and Development Inc., Istanbul, Turkey
Purpose/Objective(s): Following neoadjuvant chemo-radiotherapy (nCRT), some rectal cancer patients may have pathological lymph node (LN) involvement despite complete response in the primary tumor and should not be candidates for non-operative management. Identifying these patients may help to tailor the treatment approach. The aim of this study was to predict the rectal cancer patients with high risk of pLN involvement following nCRT, using machine learning (ML), based on pre-surgical patient-, disease- and treatment-related clinical factors. Materials/
Methods: Data of the rectal cancer patients treated with nCRT between 2013-2023 were collected. Patients with secondary pelvic malignancies, no LN dissection, RT-surgery interval>21 weeks and cLN diameter<5 mm were excluded. 143 patients were included in the analysis. Median age was 58 (26-83). Female/male ratio was 55/88. Tumor (T) location was proximal-, mid- and distal-rectum in 8, 58 and 77 patients, respectively. cT stage was T2, T3 and T4 in 9, 122 and 12 patients, respectively. 89 patients had cLN diameter=10 (10-34) mm. Except 16 (mucinous: 11, signed ring cell: 5) patients, all had adeno carcinoma NOS histology. Median pre-RT T and LN SUVmax were 15 (4-46) and 3 (1-30), respectively. Median GTV was 73 (23-370) cm3.Median T length and thickness were 6 (2-14) cm and 16 (7-48) mm, respectively. Median T and regional LN doses were 56 (45-56) Gy and 50.4 (45-50.4) Gy, respectively, in 25-28 fractions, concurrently with capecitabine. Median RT-surgery interval was 11 (4-21) weeks. 48 patients were pN (+). A ML model was used to predict pLN involvement based on 32 parameters (including age, gender, BMI, smoking, Hb, Hct, T localization, histology, GTV, and cLN diameter). Logistic regression model, configured with hyperparameters, exhibited the best performance with high accuracy and F1 scores after testing various algorithms. The performance of the model was evaluated using precision (positive predictive value), recall (sensitivity), F1 score (performance of classification), accuracy, and ROC AUC value. The model was trained using 75% of the data and the remaining 25% was used for testing. Results: Accuracy, F1 scores and ROC AUC value of the model were 85.71%, 70.59% and 0.78, respectively. Precision, recall and F1 score of the model are seen in the Table. Analysis of the confusion matrix revealed that the model successfully predicts 24 true negative and 6 true positive cases, with only 1 false positive and 4 false negatives. Similar accuracy rates on the training (84.76%) and test (85.71%) datasets were observed. Histology, BMI, Hct, Hb, and weight were the most significant parameters in the model. Conclusion: Machine learning has a high rate of correctly predicting pathological lymph node status of the rectal cancer patients following nCRT and can possibly be used in clinical practice.
