A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer - Xu, Zhiyuan; Yang, Li; Yu, Hao; Guo, Linlang (Frontiers in Oncology, 2022)
Background/Purpose: Severe lymphopenia during pelvic radiotherapy (RT) predicts poor survival in patients with cervical cancer. However, the risk of severe lymphopenia has not been well predicted. We developed a machine learning model using clinical and dosimetric information to predict grade 4 (G4) lymphopenia during pelvic RT in patients with cervical cancer. Methods: This retrospective study included cervical cancer patients treated with definitive pelvic RT ± induction/concurrent chemotherapy. Clinical information and a set of dosimetric parameters of external beam radiotherapy plan were collected. G4 lymphopenia during RT, which was also referred to as G4 absolute lymphocyte count (ALC) nadir, was defined as ALC nadir < 0.2 ×109 cells/L during RT according to Common Terminology Criteria for Adverse Events (CTCAE) v4.03. Elastic-net logistic regression models were constructed for the prediction of G4 lymphopenia during pelvic RT using a repeated cross-validation methodology. Results: A total of 130 patients were eligible and 43 (33.1%) patients had G4 lymphopenia during RT. On multivariate analysis, G4 ALC nadir was associated with poor overall survival (OS) (Hazard ratio [HR] 3.91, 95% confidence interval [CI] 1.34-11.38, P = 0.01). Seven significant factors (Eastern Cooperative Oncology Group [ECOG] performance score, pre-RT hemoglobin, pre-RT lymphocytes, concurrent chemotherapy, gross tumor volume of regional lymphadenopathy [GTV_N volume], body volume, and maximum dose of planning target volume receiving at least 55Gy [PTV_5500 Dmax]) were obtained by Elastic-net logistic regression models and were included in the final prediction model for G4 ALC nadir. The model's predicting ability was validated in the completely independent test set (area under the curve [AUC] = 0.8, accuracy = 0.79). A nomogram of the final predicting model was constructed. Conclusions: This study developed and validated a comprehensive model integrating clinical and dosimetric parameters to predict G4 lymphopenia during pelvic RT by machine learning method in patients with cervical cancer, which provides an opportunity to guide clinicians.