Advanced imaging to predict response to chemotherapy in colorectal liver metastases - a systematic review.

BACKGROUND: The assessment of colorectal liver metastases (CRLM) after treatment with chemotherapy is challenging due to morphological and/or functional change without changes in size. The aim of this review was to assess the value of FDG-PET, FDG-PET-CT, CT and MRI in predicting response to chemotherapy in CRLM. METHODS: A systematic review was undertaken based on PRISMA statement. PubMed and Embase were searched up to October 2016 for studies on the accuracy of PET, PET-CT, CT and MRI in predicting RECIST or metabolic response to chemotherapy and/or survival in patients with CRLM. Articles evaluating the assessment of response after chemotherapy were excluded. RESULTS: Sixteen studies met the inclusion criteria and were included for further analysis. Study results were available for 6 studies for FDG-PET(-CT), 6 studies for CT and 9 studies for MRI. Generally, features predicting RECIST or metabolic response often predicted shorter survival. The ADC (apparent diffusion coefficient, on MRI) seems to be the most promising predictor of response and survival. In CT-related studies, few attenuation-related parameters and texture features show promising results. In FDG-PET(-CT), findings were ambiguous. CONCLUSION: Radiological data on the prediction of response to chemotherapy for CRLM is relatively sparse and heterogeneous. Despite that, a promising parameter might be ADC. Second, there seems to be a seemingly counterintuitive correlation between parameters that predict a good response and also predict poor survival.

The liver-first approach for synchronous colorectal liver metastases: more than a decade of experience in a single centre.

BACKGROUND: The feasibility of the liver-first approach for synchronous colorectal liver metastases (CRLM) has been established. We sought to assess the short-term and long-term outcomes for these patients. METHODS: Outcomes of patients who underwent a liver-first approach for CRLM between 2005 and 2015 were retrospectively evaluated from a prospective database. RESULTS: Of the 92 patients planned to undergo the liver-first strategy, the paradigm could be completed in 76.1%. Patients with concurrent extrahepatic disease failed significantly more often in completing the protocol (67% versus 21%; p = 0.03). Postoperative morbidity and mortality were 31.5% and 3.3% following liver resection and 30.9% and 0% after colorectal surgery. Of the 70 patients in whom the paradigm was completed, 36 patients (51.4%) developed recurrent disease after a median interval of 20.9 months. The median overall survival on an intention-to-treat basis was 33.1 months (3- and 5-year overall survival: 48.5% and 33.1%). Patients who were not able to complete their therapeutic paradigm had a significantly worse overall outcome (p = 0.03). CONCLUSION: The liver-first approach is feasible with acceptable perioperative morbidity and mortality rates. Despite the considerable overall-survival-benefit, recurrence rates remain high. Future research should focus on providing selection tools to enable the optimal treatment sequence for each patient with synchronous CRLM.

Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases.

PURPOSE: Early identification of patients at risk of developing colorectal liver metastases can help personalizing treatment and improve oncological outcome. The aim of this study was to investigate in patients with colorectal cancer (CRC) whether a machine learning-based radiomics model can predict the occurrence of metachronous metastases. METHODS: In this multicentre study, the primary staging portal venous phase CT of 91 CRC patients were retrospectively analysed. Two groups were assessed: patients without liver metastases at primary staging, or during follow-up of ≥ 24 months (n = 67) and patients without liver metastases at primary staging but developed metachronous liver metastases < 24 months after primary staging (n = 24). After liver parenchyma segmentation, 1767 radiomics features were extracted for each patient. Three predictive models were constructed based on (1) radiomics features, (2) clinical features and (3) a combination of clinical and radiomics features. Stability of features across hospitals was assessed by the Kruskal-Wallis test and inter-correlated features were removed if their correlation coefficient was higher than 0.9. Bayesian-optimized random forest with wrapper feature selection was used for prediction models. RESULTS: The three predictive models that included radiomics features, clinical features and a combination of radiomics with clinical features resulted in an AUC in the validation cohort of 86% (95%CI 85-87%), 71% (95%CI 69-72%) and 86% (95% CI 85-87%), respectively. CONCLUSION: A machine learning-based radiomics analysis of routine clinical CT imaging at primary staging can provide valuable biomarkers to identify patients at high risk for developing colorectal liver metastases.

Whole liver CT texture analysis to predict the development of colorectal liver metastases-A multicentre study.

OBJECTIVES: CT texture analysis has shown promise to differentiate colorectal cancer patients with/without hepatic metastases. AIM: To investigate whether whole-liver CT texture analysis can also predict the development of colorectal liver metastases. MATERIAL AND METHODS: Retrospective multicentre study (n=165). Three subgroups were assessed: patients [A] without metastases (n=57), [B] with synchronous metastases (n=54) and [C] who developed metastases within ≤24 months (n=54). Whole-liver texture analysis was performed on primary staging CT. Mean grey-level intensity, entropy and uniformity were derived with different filters (σ0.5-2.5). Univariable logistic regression (group A vs. B) identified potentially predictive parameters, which were tested in multivariable analyses to predict development of metastases (group A vs. C), including subgroup analyses for early (≤6 months), intermediate (7-12 months) and late (13-24 months) metastases. RESULTS: Univariable analysis identified uniformity (σ0.5), sex, tumour site, nodal stage and carcinoembryonic antigen as potential predictors. Uniformity remained a significant predictor in multivariable analysis to predict early metastases (OR 0.56). None of the parameters could predict intermediate/late metastases. CONCLUSIONS: Whole-liver CT-texture analysis has potential to predict patients at risk of developing early liver metastases ≤6 months, but is not robust enough to identify patients at risk of developing metastases at later stage.