ArticleFrontiers in oncology2022
A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical challenges in the adaptation of AI predictive models in radiation oncology for gynaecological cancer: a systematic review by the radiation oncology-AI MITO group.La Radiologia medica · 2026Pooled it
- Preoperative prediction of cervical cancer recurrence using explainable MRI radiomics: a SHAP-Guided machine learning study.Abdominal radiology (New York) · 2026Article
- Immunogenic cell death as a cornerstone for combination therapies with immune checkpoint blockade.Molecular cancer · 2026Review
- Prediction of myelosuppression risk in postoperative cervical cancer patients undergoing concurrent chemoradiotherapy using machine learning models.Frontiers in oncology · 2026Article
- Radiation-induced lymphopenia: A data compilation to unveil relevant factors and mitigation strategies.Clinical and translational radiation oncology · 2026Review
- The Role of Lymphocyte Recovery Index in Prognosis Prediction for Locally Advanced Cervical Cancer With Radiation-Induced Lymphopenia.Cancer medicine · 2025Article
- Development and validation of a predictive model for the risk of endocervical curettage positivity.Frontiers in oncology · 2025Article
- Article
- Early lymphocyte levels and low doses radiation exposure of lung predict lymphopenia in radiotherapy for lung cancer.Frontiers in immunology · 2024Observational
- Predictive value of bowel dose-volume for severe radiation-induced lymphopenia and survival in cervical cancer.Frontiers in immunology · 2024Article
- Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning.Frontiers in neurology · 2024Article
- Trial watch: chemotherapy-induced immunogenic cell death in oncology.Oncoimmunology · 2023Review
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4 authors.
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Abstract
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 × 10 Results: A total of 130 patients were eligible, and 43 (33.1%) patients had G4 lymphopenia during RT. On multivariable 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 55 Gy (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 in test set was area under the curve (AUC) = 0.77 and accuracy = 0.76. A nomogram of the final predicting model was constructed. Conclusions: This study developed and validated a comprehensive model integrating clinical and dosimetric parameters by machine learning method, which performed well in predicting G4 lymphopenia during pelvic RT for cervical cancer and will facilitate physicians to identify patients at high risk of G4 lymphopenia who might benefit from modified treatment approaches.
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