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).
Highlights
Cervical cancer, which is a main health problem for women, is one of the most common malignant tumours in gynaecology and ranks fourth among all malignant tumours [1–3]
According to the International Federation of Obstetrics and Gynaecology (FIGO) staging of cervical cancer, surgical resection is often used for early cervical cancer and concurrent chemoradiotherapy (CCRT) is often given clinically for middle and advanced cervical cancer, which usually loses the opportunity for radical surgical treatment
Considerable radiomics analyses have been performed on diffusion-weighted imaging (DWI) and DCE-magnetic resonance imaging (MRI) but rarely on T2WI [25–28]
Summary
Cervical cancer, which is a main health problem for women, is one of the most common malignant tumours in gynaecology and ranks fourth among all malignant tumours [1–3]. According to the International Federation of Obstetrics and Gynaecology (FIGO) staging of cervical cancer, surgical resection is often used for early cervical cancer and concurrent chemoradiotherapy (CCRT) is often given clinically for middle and advanced cervical cancer, which usually loses the opportunity for radical surgical treatment. Chemoradiotherapy improves the survival rate of advanced cervical cancer, several patients are still treated with poor efficacy [4]. Accurate prediction can provide decision basis for drug treatment and avoid incorrect medication and provide guidance for patients insensitive to conventional treatment to change the drug type, adjust radiation dose and CCRT regimen or receive surgery immediately to avoid delayed treatment opportunity and waste of time and money [6, 7]. The accurate prediction of the sensitivity and tolerance of tumour cells is the key to clinical treatment
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