Dose prediction of organs at risk in patients with cervical cancer receiving brachytherapy using needle insertion based on a neural network method

• Published in: BMC cancer Vol. 23; no. 1; p. 385
• Main Authors: Zhang, Huai-Wen, Zhong, Xiao-Ming, Zhang, Zhen-Hua, Pang, Hao-Wen
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28.04.2023; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Biopsy, Needle; Bladder; Brachytherapy; Brachytherapy - adverse effects; Brachytherapy - methods; Cancer therapies; Care and treatment; Cervical cancer; Colon; Diagnosis; Dose prediction; Dosimetry; Female; Gynecology; Humans; Methods; Needle insertion; Neural network; Neural networks; Neural Networks, Computer; Optimization; Organs at Risk; Patient outcomes; Patients; Planning; Prediction models; Radiation therapy; Radioisotope brachytherapy; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted - methods; Rectum; Software; Tumors; Uterine Cervical Neoplasms - etiology; Uterine Cervical Neoplasms - radiotherapy; Netherlands; Dose prediction; Brachytherapy; Neural network; Needle insertion
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-023-10875-6

A neural network method was employed to establish a dose prediction model for organs at risk (OAR) in patients with cervical cancer receiving brachytherapy using needle insertion. A total of 218 CT-based needle-insertion brachytherapy fraction plans for loco-regionally advanced cervical cancer treatment were analyzed in 59 patients. The sub-organ of OAR was automatically generated by self-written MATLAB, and the volume of the sub-organ was read. Correlations between D2cm of each OAR and volume of each sub-organ-as well as high-risk clinical target volume for bladder, rectum, and sigmoid colon-were analyzed. We then established a neural network predictive model of D2cm of OAR using the matrix laboratory neural net. Of these plans, 70% were selected as the training set, 15% as the validation set, and 15% as the test set. The regression R value and mean squared error were subsequently used to evaluate the predictive model. The D2cm /D90 of each OAR was related to volume of each respective sub-organ. The R values for bladder, rectum, and sigmoid colon in the training set for the predictive model were 0.80513, 0.93421, and 0.95978, respectively. The ∆D2cm /D90 for bladder, rectum, and sigmoid colon in all sets was 0.052 ± 0.044, 0.040 ± 0.032, and 0.041 ± 0.037, respectively. The MSE for bladder, rectum, and sigmoid colon in the training set for the predictive model was 4.779 × 10 , 1.967 × 10 and 1.574 × 10 , respectively. The neural network method based on a dose-prediction model of OAR in brachytherapy using needle insertion was simple and reliable. In addition, it only addressed volumes of sub-organs to predict the dose of OAR, which we believe is worthy of further promotion and application.