Dataset Information

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases.

ABSTRACT: The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R-) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R- lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies.

SUBMITTER: Giannini V

PROVIDER: S-EPMC8750408 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases.

Giannini Valentina V Pusceddu Laura L Defeudis Arianna A Nicoletti Giulia G Cappello Giovanni G Mazzetti Simone S Sartore-Bianchi Andrea A Siena Salvatore S Vanzulli Angelo A Rizzetto Francesco F Fenocchio Elisabetta E Lazzari Luca L Bardelli Alberto A Marsoni Silvia S Regge Daniele D

Cancers 20220104 1

The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R-) if they s ...[more]

PMID: 35008405

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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:BackgroundMetastasectomy is probably underused in metastatic colorectal cancer. The aim of this study was to investigate the effect of centralized repeated assessment on resectability rate of liver metastases.MethodsThe prospective RAXO study was a nationwide study in Finland. Patients with treatable metastatic colorectal cancer at any site were eligible. This planned substudy included patients with baseline liver metastases between 2012 and 2018. Resectability was reassessed by the multidisciplinary team at Helsinki tertiary referral centre upfront and twice during first-line systemic therapy. Outcomes were resectability rates, management changes, and survival.ResultsOf 812 patients included, 301 (37.1 per cent) had liver-only metastases. Of these, tumours were categorized as upfront resectable in 161 (53.5 per cent), and became amenable to surgery during systemic treatment in 63 (20.9 per cent). Some 207 patients (68.7 per cent) eventually underwent liver resection or ablation. At baseline, a discrepancy in resectability between central and local judgement was noted for 102 patients (33.9 per cent). Median disease-free survival (DFS) after first resection was 20 months and overall survival (OS) 79 months. Median OS after diagnosis of metastatic colorectal cancer was 80, 32, and 21 months in R0-1 resection, R2/ablation, and non-resected groups, and 5-year OS rates were 68, 37, and 9 per cent, respectively. Liver and extrahepatic metastases were present in 511 patients. Of these, tumours in 72 patients (14.1 per cent) were categorized as upfront resectable, and 53 patients (10.4 per cent) became eligible for surgery. Eventually 110 patients (21.5 per cent) underwent liver resection or ablation. At baseline, a discrepancy between local and central resectability was noted for 116 patients (22.7 per cent). Median DFS from first resection was 7 months and median OS 55 months. Median OS after diagnosis of metastatic colorectal cancer was 79, 42, and 17 months in R0-1 resection, R2/ablation, and non-resected groups, with 5-year OS rates of 65, 39, and 2 per cent, respectively.ConclusionRepeated centralized resectability assessment in patients with colorectal liver metastases improved resection and survival rates.

Dataset Information

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases.

Publications

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases.

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