Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

Magnetic resonance imaging-radiomics evaluation of response to chemotherapy for synchronous liver metastasis of colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is an indicator of poor prognosis for colorectal cancer(CRC).Nearly 50%of CRC patients develop hepatic metastasis,with 15%-25%of them presenting with SLM.The evaluation of SLM in CRC is crucial for precise and personalized treatment.It is beneficial to detect its response to chemotherapy and choose an optimal treatment method.AIM To construct prediction models based on magnetic resonance imaging(MRI)-radiomics and clinical parameters to evaluate the chemotherapy response in SLM of CRC.METHODS A total of 102 CRC patients with 223 SLM lesions were identified and divided into disease response(DR)and disease non-response(non-DR)to chemotherapy.After standardizing the MRI images,the volume of interest was delineated and radiomics features were calculated.The MRI-radiomics logistic model was constructed after methods of variance/Mann-Whitney U test,correlation analysis,and least absolute shrinkage and selection operator in feature selecting.The radiomics score was calculated.The receiver operating characteristics curves by the DeLong test were analyzed with MedCalc software to compare the validity of all models.Additionally,the area under curves(AUCs)of DWI,T2WI,and portal phase of contrast-enhanced sequences radiomics model(Ra-DWI,Ra-T2WI,and Ra-portal phase of contrast-enhanced sequences)were calculated.The radiomicsclinical nomogram was generated by combining radiomics features and clinical characteristics of CA19-9 and clinical N staging.RESULTS The AUCs of the MRI-radiomics model were 0.733 and 0.753 for the training(156 lesions with 68 non-DR and 88 DR)and the validation(67 lesions with 29 non-DR and 38 DR)set,respectively.Additionally,the AUCs of the training and the validation set of Ra-DWI were higher than those of Ra-T2WI and Ra-portal phase of contrast-enhanced sequences(training set:0.652 vs 0.628 and 0.633,validation set:0.661 vs 0.575 and 0.543).After chemotherapy,the top four of twelve deltaradiomics features of Ra-DWI in the DR group belonged to gray-level run-length matrices radiomics parameters.The radiomics-clinical nomogram containing radiomics score,CA19-9,and clinical N staging was built.This radiomics-clinical nomogram can effectively discriminate the patients with DR from non-DR with a higher AUC of 0.809(95%confidence interval:0.751-0.858).CONCLUSION MRI-radiomics is conducive to predict chemotherapeutic response in SLM patients of CRC.The radiomics-clinical nomogram,involving radiomics score,CA19-9,and clinical N staging is more effective in predicting chemotherapeutic response.

Radiomics-clinical nomogram for response to chemotherapy in synchronous liver metastasis of colorectal cancer:Good,but not good enough

There remains a persistent unmet need to detect the disease nonresponse(nonDR)subgroup before adjuvant therapy in synchronous liver metastasis patients with colorectal cancer.Ma’s radiomics-clinical nomogram shows potential for the early detection of nonDR subgroups,but it is not good enough owing to at least three limitaions,which we address in this letter to the editor.First,the study did not explore RAS/BRAF mutations,HER2 amplifications,etc.to complement the current nomogram.Second,the nomogram was not validated in left-and rightsided tumors separately.Third,the most critical factor for determining the success of adjuvant therapy should be resectability rather than tumor size shrinkage,which was used in the study.

Is CA19-9 effective in predicting chemotherapeutic response in patients with synchronous liver metastases with colorectal cancer?

Evaluation of response to chemotherapy in colorectal cancer patients with synchronous liver metastases is important in terms of treatment management.In this Letter to the Editor,several issues in the article are discussed.For the comparison of carbohydrate antigen 19-9(CA19-9)values referenced in the study,the patient group was not matched for cancer stage.Therefore,it may be more appropriate to select and compare CA19-9 values in patients with same-stage cancer.