Objective:

To investigate the effectiveness of predicting the efficacy of neo-adjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer based on different radiomics models based on apparent diffusion coefficient (ADC) map.

Methods:

A retrospective analysis of 43 non-metastatic locally advanced rectal cancer patients from Mar. 2017 to May. 2019 was carried. All patients underwent nCRT

and before and after treatment underwent MRI. ITK-SNAP was used to manually outline the region of interest (ROI) on the ADC map before treatment

and 109 imaging omics features were extracted by the imaging omics softwarePyradiomics. Forty-three patients used paired-difference analysis (PDA) to increase the sample size

and a total of 378 sample pairs were obtained

which were randomly divided into training and test groups according to 7∶3. Support vector machine (SVM)

auto-encoder (AE)

linear discriminant analysis (LDA)

random forest (RF)

and logistic regression (LR) And LR-Lasso (logistic regression via Lasso). According to the accuracy

sensitivity

and specificity of the model on the test set

the optimal combination of a model is determined

and the receiver operating characteristic curve analysis is used to evaluate the diagnostic performance of different models. The analysis model was developed based on Sklearn and FeAture Explorer.

Results:

Finally

the three more stable models were screened: SVM

RF

and LR-Lasso models. The SVM model had an AUC

accuracy of 0.934 and 98.4%

sensitivity and specificity of 80.0% and 100.0%; negative predictive value and positive predictive value were 98.3% and 100.0%. The RF model had AUC

accuracy of 0.998 and 98.4%

sensitivity and specificity of 100.0% and 98.3%

negative predictive value and positive predictive value of 100.0% and 83.2%. The LR-Lasso model has AUC

accuracy of 0.997 and 98.4%

sensitivity and specificity of 100.0% and 98.3%

negative predictive value and positive predictive value of 100.0% and 83.3%.

Conclusion:

The Radiomics model has a higher accuracy in predicting the efficacy of locally advanced rectal cancer. The Radiomics model established by the RF method has higher diagnostic efficiency than other omics models.