Feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy

Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy

The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam computed tomography (CBCT) images could be used to predict the target, i.e. clinical target volume (CTV) shifts, with small errors. The pCT and daily CBCT images of 20 patients with prostate cancer were selected. The first 10 patients were employed for the development, and the second 10 patients for a validation test. The CTV position errors between the pCT and CBCT images were determined as reference CTV shifts (teacher data) after an automated bone-based registration. The anatomical features associated with rectum, bladder and prostate were calculated from the pCT and CBCT images. The features were fed as the input with the teacher data into five MLAs, i.e. three types of artificial neural networks, support vector regression (SVR) and random forests. Since the CTV shifts along the left-right direction were negligible, the MLAs were developed along the superior-inferior and anterior-posterior directions. The proposed framework was evaluated from the residual errors between the reference and predicted CTV shifts. In the validation test, the mean residual error with its standard deviation was 1.01 ± 1.09 mm in SVR using only one feature (one click), which was associated with positional difference of the upper rectal wall. The results suggested that MLAs with anatomical features could be useful in prediction of CTV shifts for prostate radiotherapy.

Homological radiomics analysis for prognostic prediction in lung cancer patients

PURPOSE

This study explored a novel homological analysis method for prognostic prediction in lung cancer patients.

MATERIALS AND METHODS

The potential of homology-based radiomic features (HFs) was investigated by comparing HFs to conventional wavelet-based radiomic features (WFs) and combined radiomic features consisting of HFs and WFs (HWFs), using training (n = 135) and validation (n = 70) datasets, and Kaplan-Meier analysis. A total of 13,824 HFs were derived through homology-based texture analysis using Betti numbers, which represent the topologically invariant morphological characteristics of lung cancer. The prognostic potential of HFs was evaluated using statistically significant differences (p-values, log-rank test) to compare the survival curves of high- and low-risk patients. Those patients were stratified into high- and low-risk groups using the medians of the radiomic scores of signatures constructed with an elastic-net-regularized Cox proportional hazard model. Furthermore, deep learning (DL) based on AlexNet was utilized to compare HFs by stratifying patients into the two groups using a network that was pre-trained with over one million natural images from an ImageNet database.

RESULTS

For the training dataset, the p-values between the two survival curves were 6.7 × 10^-6 (HF), 5.9 × 10^-3 (WF), 7.4 × 10^-6 (HWF), and 1.1 × 10^-3 (DL). The p-values for the validation dataset were 3.4 × 10^-5 (HF), 6.7 × 10^-1 (WF), 1.7 × 10^-7 (HWF), and 1.2 × 10^-1 (DL).

CONCLUSION

This study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.