Dataset Information

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

ABSTRACT: OBJECTIVES:This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). METHODS:Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R2. Clinicopatholologic factors were assessed for correlation with response. RESULTS:157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). CONCLUSION:Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. KEY POINTS:• Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

SUBMITTER: Creasy JM

PROVIDER: S-EPMC6301122 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

Creasy John M JM Midya Abhishek A Chakraborty Jayasree J Adams Lauryn B LB Gomes Camilla C Gonen Mithat M Seastedt Kenneth P KP Sutton Elizabeth J EJ Cercek Andrea A Kemeny Nancy E NE Shia Jinru J Balachandran Vinod P VP Kingham T Peter TP Allen Peter J PJ DeMatteo Ronald P RP Jarnagin William R WR D'Angelica Michael I MI Do Richard K G RKG Simpson Amber L AL

European radiology 20180619 1

<h4>Objectives</h4>This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM).<h4>Methods</h4>Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour v ...[more]

PMID: 29922934

Similar Datasets

Project description:Predicting the prognosis of patients in advance is conducive to providing personalized treatment for patients. Our aim was to predict the therapeutic efficacy and progression free survival (PFS) of patients with liver metastasis of colorectal cancer according to the changes of computed tomography (CT) radiomics before and after chemotherapy.MethodsThis retrospective study included 139 patients (397 lesions) with colorectal liver metastases who underwent neoadjuvant chemotherapy from April 2015 to April 2020. We divided the lesions into training cohort and testing cohort with a ratio of 7:3. Two - dimensional region of interest (ROI) was obtained by manually delineating the largest layers of each metastasis lesion. The expanded ROI (3 mm and 5 mm) were also included in the study to characterize microenvironment around tumor. For each of the ROI, 1,316 radiomics features were extracted from delineated plain scan, arterial, and venous phase CT images before and after neoadjuvant chemotherapy. Delta radiomics features were constructed by subtracting the radiomics features after treatment from the radiomics features before treatment. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were applied in the training cohort to select the valuable features. Based on clinical characteristics and radiomics features, 7 Cox proportional-hazards model were constructed to predict the PFS of patients. C-index value and Kaplan Meier (KM) analysis were used to evaluate the efficacy of predicting PFS of these models. Moreover, the prediction performance of one-year PFS was also evaluated by area under the curve (AUC).ResultsCompared with the PreRad (Radiomics form pre-treatment CT images; C-index [95% confidence interval (CI)] in testing cohort: 0.614(0.552-0.675) and PostRad models (Radiomics form post-treatment CT images; 0.642(0.578-0.707), the delta model has better PFS prediction performance (Delta radiomics; 0.688(0.627-0.749). By incorporating clinical characteristics, CombDeltaRad obtains the best performance in both training cohort [C-index (95% CI): 0.802(0.772-0.832)] and the testing cohort (0.744(0.686-0.803). For 1-year PFS prediction, CombDeltaRad model obtained the best performance with AUC (95% CI) of 0.871(0.828-0.914) and 0.745 (0.651-0.838) in training cohort and testing cohort, respectively.ConclusionCT radiomics features have the potential to predict PFS in patients with colorectal cancer and liver metastasis who undergo neoadjuvant chemotherapy. By combining pre-treatment radiomics features, post-treatment radiomics features, and clinical characteristics better prediction results can be achieved.

Project description:BackgroundTo develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery.MethodsThis study retrospectively analyzed 112 RC patients without preoperative liver metastases who underwent rectal surgery between January 2015 and December 2017 at our institution. Volume of interest (VOI) segmentation of the whole-liver was performed on the PVP CE-CT images. All 1316 radiomics features were extracted automatically. The maximum-relevance and minimum-redundancy and least absolute shrinkage and selection operator methods were used for features selection and radiomics signature constructing. Three models based on radiomics features (radiomics model), clinical features (clinical model), and radiomics combined with clinical features (combined model) were built by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of models, and calibration curve and the decision curve analysis were performed to evaluate the clinical application value.ResultsIn total, 52 patients in the MLM group and 60 patients in the non-MLM group were enrolled in this study. The radscore was built using 16 selected features and the corresponding coefficients. Both the radiomics model and the combined model showed higher diagnostic performance than clinical model (AUCs of training set: radiomics model 0.84 (95% CI, 0.76-0.93), clinical model 0.65 (95% CI, 0.55-0.75), combined model 0.85 (95% CI, 0.77-0.94); AUCs of validation set: radiomics model 0.84 (95% CI, 0.70-0.98), clinical model 0.58 (95% CI, 0.40-0.76), combined model 0.85 (95% CI, 0.71-0.99)). The calibration curves showed great consistency between the predicted value and actual event probability. The DCA showed that both the radiomics and combined models could add a net benefit on a large scale.ConclusionsThe radiomics model based on preoperative whole-liver PVP CE-CT could predict MLM within 24 months after RC surgery. Clinical features could not significantly improve the prediction efficiency of the radiomics model.

Project description:Rationale: Hepatectomy and adjuvant chemotherapy after resection of colorectal liver metastases (CRLM) may improve survival, however, patients which may benefit cannot currently be identified. Postoperative circulating tumor DNA (ctDNA) analysis can detect minimal residual disease (MRD) and predict the prognosis and efficacy of adjuvant chemotherapy. Our study aims to determine the impact of serial ctDNA analysis to predict the outcome among patients undergoing resection of CRLM. Methods: Between May 2018 and October 2019, 91 CRLM patients were prospectively enrolled. Whole exome sequencing was performed in 50 primary and 48 metastatic liver tissues. Targeted sequencing of 451 cancer relevant genes was performed in 50 baseline plasma to determine plasma-tissue concordance. We prospectively investigated changes in the amount and constitution of ctDNA in 271 serial plasma samples taken at different time points (baseline, pre-operation, post-operation, post-operative adjuvant chemotherapy (post-ACT) and recurrence) during the treatment of CRLM. Results: Detected molecular alterations were highly consistent among baseline ctDNA, primary and liver metastases tissue. Patients with a higher variant allele frequency (VAF) level at baseline ctDNA represent a higher tumor burden, and decreased ctDNA during pre-operative chemotherapy predicted better tumor response. Patients with detectable post-operative and post-ACT ctDNA were associated with significantly shorter recurrence-free survival (RFS). ROC analysis showed that post-ACT ctDNA status was superior to post-operative ctDNA status in predicting RFS with an AUROC of 0.79. A significant difference in overall recurrence rate was observed in patients with detectable vs undetectable levels of ctDNA after resection of CRLM (79.4% vs 41.7%) and after completion of adjuvant chemotherapy (77.3% vs 40.7%). During adjuvant chemotherapy, patients with decreased ctDNA VAF after adjuvant chemotherapy had a recurrence rate of 63.6%, compared to 92.3% in patients with increased ctDNA VAF. Conclusions: We envision that dynamic ctDNA analysis, especially in a post-ACT setting, might be used to not only reflect MRD but also to determine rational personalized adjuvant therapy after the resection of CRLM.

Dataset Information

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

Publications

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets