Abstract 1412: CT texture analysis to predict response to target therapy of hepatic metastases from colorectal cancer

e15086 Background: Metastatic Colorectal cancer (mCRC) is the 2nd cause of cancer death worldwide. Repeated cycles of therapies, combined with surgery in oligo-metastatic cases, are the therapeutic standard in mCRC. However, this strategy is seldom resolutive. Different lesions in in the same patient could have different responses to systemic therapy. Recently, CT texture analysis (CTTA) had been shown to potentially provide with prognostic and predictive markers, overcoming the limitations of biopsy sampling in defining tumor heterogeneity. The aim of this study is to use CT texture analysis (CTTA) to identify imaging biomarkers of HER2+ mCRC able to predict lesion response to therapy. Methods: The dataset is composed of 39 extended RAS wild type patients with amplified HER2 mCRC enrolled in the HERACLES trial (NCT03225937) that received either a lapatinib+trastuzumab treatment (n = 23) or a pertuzumab+ trastuzumab-emtansine treatment (n = 16). All patients underwent CT examination every 8 weeks, until disease progression. All mCRC on baseline CT were semi-automatically segmented and quantitative features extracted: size, mean, percentiles, 28 texture features. A logistic regression model was created using: (i) the whole dataset of mCRC as training and test set and (ii) 100 randomly generated training sets (with 70% of responder (R+) mCRC and an equal number of non-responder (R-) mCRC), and 100 test sets including the remaining mCRC. A mCRC was classified as R+ if size decreased (-10%) or was stable (±10%); as R- if size increased (+10%), during subsequent CT scans. Results: A total of 199 metastases were included (75R+ and 124R-). The training set was composed of 53R+ and 53R- mCRC and the test set of 22R+ and 71R- mCRC. Using the whole dataset, the model reached an AUC = 0.82 (sensitivity = 84%, specificity = 70%), while it reached a mean AUC of 0.70 (sensitivity = 68%, specificity = 67%) within the 100 repetitions. Conclusions: CTTA might help in stratifying different behaviors of mCRC, opening the way for lesion-specific therapies, with conceivable cost and life savings. Further extended analysis is needed to better characterize and validate predictive value of these biomarkers.

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CT texture analysis as a prognostic marker in metastatic colorectal cancer patients treated with bevacizumab

Cancer Imaging ◽

10.1186/1470-7330-15-s1-s12 ◽

2015 ◽

Vol 15 (S1) ◽

Author(s):

Shih-Hsin Chen ◽

Julien Edeline ◽

Kai-Keen Shiu ◽

Sarah Benafif ◽

Sofia Wong ◽

...

Keyword(s):

Colorectal Cancer ◽

Metastatic Colorectal Cancer ◽

Cancer Patients ◽

Texture Analysis ◽

Prognostic Marker ◽

Ct Texture Analysis ◽

Colorectal Cancer Patients

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CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer

Chinese Medical Sciences Journal ◽

10.24920/003770 ◽

2020 ◽

Vol 35 (4) ◽

pp. 306

Author(s):

Cao Jian ◽

Wang Guorong ◽

Wang Zhiwei ◽

Jin Zhengyu

Keyword(s):

Colorectal Cancer ◽

Texture Analysis ◽

Mutational Status ◽

Potential Biomarker ◽

Ct Texture Analysis

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Machine learning for CT texture analysis in chemosensitivity of colorectal liver metastases: Initial results.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e15148 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e15148-e15148

Author(s):

Yuji Miyamoto ◽

Takeshi Nakaura ◽

Yukiharu Hiyoshi ◽

Hideo Baba

Keyword(s):

Colorectal Cancer ◽

Machine Learning ◽

Prediction Model ◽

Liver Metastases ◽

Texture Analysis ◽

Gray Level ◽

Learning Methods ◽

Machine Learning Methods ◽

Chemotherapeutic Response ◽

Ct Texture Analysis

e15148 Background: Despite advances in cancer treatment over the last decades, more efficacious biomarkers are needed in patients with metastatic colorectal cancer. Several studies have reported that CT texture analysis is a useful prognostic biomarker for patients with colorectal cancer liver metastases (CRLM), however, little study has been done to explore those efficacies using machine learning methods. The present study aimed to evaluate the clinical efficacy of CT texture analysis using machine learning methods as a predictive marker of systemic chemotherapy in patients with CRLM. Methods: Sixty-four patients with CRLM who received first-line chemotherapy were included. Texture analysis was performed on 92 features (First Order Statistics, Gray Level Cooccurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Neighbouring Gray Tone Difference Matrix and Gray Level Dependence Matrix) using CT within 1 month before treatment. We evaluated the association between those features and chemotherapeutic response by RECIST (CR+PR vs. SD+PD+NE). We performed eXtreme gradient boost (XGBoost) as a machine learning method to predict the chemotherapeutic response and used the receiver operating characteristic curves to evaluate this prediction model. Results: Main characteristics were the following: male/female = 36/28; median age = 63.5. Patients were treated with oxaliplatin-based chemotherapy (80% of patients), bevacizumab (77%) and anti-EGFR antibody (23%). Thirty-nine patients had confirmed responders, for an overall response rate of 61%, whereas 25 patients (39%) were classified as non-responders (CR: PR: SD: PD: NE = 0: 39: 20: 4: 1). The area under curve of this prediction model was 0.771. Conclusions: We confirmed that CT texture analysis using machine learning for CRLM was feasible. Further analyses are ongoing.

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Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab

Gut ◽

10.1136/gutjnl-2018-316407 ◽

2019 ◽

Vol 69 (3) ◽

pp. 531-539 ◽

Cited By ~ 19

Author(s):

Anthony Dohan ◽

Benoit Gallix ◽

Boris Guiu ◽

Karine Le Malicot ◽

Caroline Reinhold ◽

...

Keyword(s):

Colorectal Cancer ◽

Texture Analysis ◽

Validation Cohort ◽

External Validation ◽

Hepatic Metastases ◽

Survival Prediction ◽

Training Cohort ◽

Link Type ◽

Contrast Enhanced Ct ◽

Morphological Criteria

PurposeThe objective of this study was to build and validate a radiomic signature to predict early a poor outcome using baseline and 2-month evaluation CT and to compare it to the RECIST1·1 and morphological criteria defined by changes in homogeneity and borders.MethodsThis study is an ancillary study from the PRODIGE-9 multicentre prospective study for which 491 patients with metastatic colorectal cancer (mCRC) treated by 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) and bevacizumab had been analysed. In 230 patients, computed texture analysis was performed on the dominant liver lesion (DLL) at baseline and 2 months after chemotherapy. RECIST1·1 evaluation was performed at 6 months. A radiomic signature (Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced CT TextuRe Analysis (SPECTRA) Score) combining the significant predictive features was built using multivariable Cox analysis in 120 patients, then locked, and validated in 110 patients. Overall survival (OS) was estimated with the Kaplan-Meier method and compared between groups with the logrank test. An external validation was performed in another cohort of 40 patients from the PRODIGE 20 Trial.ResultsIn the training cohort, the significant predictive features for OS were: decrease in sum of the target liver lesions (STL), (adjusted hasard-ratio(aHR)=13·7, p=1·93×10–7), decrease in kurtosis (ssf=4) (aHR=1·08, p=0·001) and high baseline density of DLL, (aHR=0·98, p<0·001). Patients with a SPECTRA Score >0·02 had a lower OS in the training cohort (p<0·0001), in the validation cohort (p<0·0008) and in the external validation cohort (p=0·0027). SPECTRA Score at 2 months had the same prognostic value as RECIST at 6 months, while non-response according to RECIST1·1 at 2 months was not associated with a lower OS in the validation cohort (p=0·238). Morphological response was not associated with OS (p=0·41).ConclusionA radiomic signature (combining decrease in STL, density and computed texture analysis of the DLL) at baseline and 2-month CT was able to predict OS, and identify good responders better than RECIST1.1 criteria in patients with mCRC treated by FOLFIRI and bevacizumab as a first-line treatment. This tool should now be validated by further prospective studies.Trial registrationClinicaltrial.gov identifier of the PRODIGE 9 study: NCT00952029.Clinicaltrial.gov identifier of the PRODIGE 20 study: NCT01900717.

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