ArticleArchives of gynecology and obstetrics2021
Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images.
Article in Archives of gynecology and obstetrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 31 citations in OpenAlex.
- Prediction of therapeutic response of unresectable hepatocellular carcinoma to hepatic arterial infusion chemotherapy based on pretherapeutic MRI radiomics and Albumin-Bilirubin score.Journal of cancer research and clinical oncology · 2023Trial
- Development and validation of a PD-L1 and tumor mutational burden-based predictive model for pathological complete response after neoadjuvant immunotherapy in resectable lung cancer.International journal of clinical pharmacy · 2026Article
- 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 · 2026Review
- Prediction of myelosuppression in cervical cancer after concurrent chemoradiotherapy by CT radiomics-based model.BMC medical imaging · 2026Article
- Data-driven strategies for immunoradiotherapy in uveal melanoma: the role of artificial intelligence.Frontiers in pharmacology · 2026Review
- A radiomics-deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer.Frontiers in oncology · 2026Article
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- Computed tomography-based radiomics modeling to predict patient overall survival in cervical cancer with intensity-modulated radiotherapy combined with concurrent chemotherapy.The Journal of international medical research · 2025Observational
- Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indicators.Frontiers in oncology · 2024Article
- Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison.Scientific reports · 2023Article
- Intracavitary brachytherapy with additional Heyman capsules in the treatment of cervical cancer.Archives of gynecology and obstetrics · 2023Article
- Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network.Frontiers in oncology · 2023Article
- Evaluating Histological Subtypes Classification of Primary Lung Cancers on Unenhanced Computed Tomography Based on Random Forest Model.Journal of healthcare engineering · 2023Article
- MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018.Diagnostics (Basel, Switzerland) · 2022Article
- Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery.World journal of clinical cases · 2021Article
- Structured Reporting of Computed Tomography in the Staging of Neuroendocrine Neoplasms: A Delphi Consensus Proposal.Frontiers in endocrinology · 2021Article
- Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeOur objective was to establish a random forest model and to evaluate its predictive capability of the treatment effect of neoadjuvant chemotherapy-radiation therapy.
methodsThis retrospective study included 82 patients with locally advanced cervical cancer who underwent scanning from March 2013 to May 2018. The random forest model was established and optimised based on the open source toolkit scikit-learn. Byoptimising of the number of decision trees in the random forest, the criteria for selecting the final partition index and the minimum number of samples partitioned by each node, the performance of random forest in the prediction of the treatment effect of neoadjuvant chemotherapy-radiation therapy on advanced cervical cancer (> IIb) was evaluated.
resultsThe number of decision trees in the random forests influenced the model performance. When the number of decision trees was set to 10, 25, 40, 55, 70, 85 and 100, the performance of random forest model exhibited an increasing trend first and then a decreasing one. The criteria for the selection of final partition index showed significant effects on the generation of decision trees. The Gini index demonstrated a better effect compared with information gain index. The area under the receiver operating curve for Gini index attained a value of 0.917.
conclusionThe random forest model showed potential in predicting the treatment effect of neoadjuvant chemotherapy-radiation therapy based on high-resolution T2WIs for advanced cervical cancer (> IIb).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.