CT radiomics models are unable to predict new liver metastasis after successful thermal ablation of colorectal liver metastases.

BACKGROUND: Patients with colorectal liver metastases (CRLM) who undergo thermal ablation are at risk of developing new CRLM after ablation. Identification of these patients might enable individualized treatment. PURPOSE: To investigate whether an existing machine-learning model with radiomics features based on pre-ablation computed tomography (CT) images of patients with colorectal cancer can predict development of new CRLM. MATERIAL AND METHODS: In total, 94 patients with CRLM who were treated with thermal ablation were analyzed. Radiomics features were extracted from the healthy liver parenchyma of CT images in the portal venous phase, before thermal ablation. First, a previously developed radiomics model (Original model) was applied to the entire cohort to predict new CRLM after 6 and 24 months of follow-up. Next, new machine-learning models were developed (Radiomics, Clinical, and Combined), based on radiomics features, clinical features, or a combination of both. RESULTS: The external validation of the Original model reached an area under the curve (AUC) of 0.57 (95% confidence interval [CI]=0.56-0.58) and 0.52 (95% CI=0.51-0.53) for 6 and 24 months of follow-up. The new predictive radiomics models yielded a higher performance at 6 months compared to 24 months. For the prediction of CRLM at 6 months, the Combined model had slightly better performance (AUC=0.60; 95% CI=0.59-0.61) compared to the Radiomics and Clinical models (AUC=0.55-0.57), while all three models had a low performance for the prediction at 24 months (AUC=0.52-0.53). CONCLUSION: Both the Original and newly developed radiomics models were unable to predict new CLRM based on healthy liver parenchyma in patients who will undergo ablation for CRLM.

CT-based radiomics to distinguish progressive from stable neuroendocrine liver metastases treated with somatostatin analogues: an explorative study.

BACKGROUND: Accurate response evaluation in patients with neuroendocrine liver metastases (NELM) remains a challenge. Radiomics has shown promising results regarding response assessment. PURPOSE: To differentiate progressive (PD) from stable disease (SD) with radiomics in patients with NELM undergoing somatostatin analogue (SSA) treatment. MATERIAL AND METHODS: A total of 46 patients with histologically confirmed gastroenteropancreatic neuroendocrine tumors (GEP-NET) with ≥1 NELM and ≥2 computed tomography (CT) scans were included. Response was assessed with Response Evaluation Criteria in Solid Tumors (RECIST1.1). Hepatic target lesions were manually delineated and analyzed with radiomics. Radiomics features were extracted from each NELM on both arterial-phase (AP) and portal-venous-phase (PVP) CT. Multiple instance learning with regularized logistic regression via LASSO penalization (with threefold cross-validation) was used to classify response. Three models were computed: (i) AP model; (ii) PVP model; and (iii) AP + PVP model for a lesion-based and patient-based outcome. Next, clinical features were added to each model. RESULTS: In total, 19 (40%) patients had PD. Median follow-up was 13 months (range 1-50 months). Radiomics models could not accurately classify response (area under the curve 0.44-0.60). Adding clinical variables to the radiomics models did not significantly improve the performance of any model. CONCLUSION: Radiomics features were not able to accurately classify response of NELM on surveillance CT scans during SSA treatment.

Magnetic resonance assessment of sinusoidal obstruction syndrome after neoadjuvant chemotherapy for colorectal liver metastases is not reproducible.

BACKGROUND: Sinusoidal obstruction syndrome (SOS) due to chemotherapy can cause severe hepatotoxicity, leading to impaired outcome in patients with colorectal cancer. A previous study introduced gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI) to diagnose SOS. PURPOSE: To assess the reproducibility of Gd-EOB-MRI-based SOS diagnosis and its relationship with response to chemotherapy and long-term outcome. MATERIAL AND METHODS: Twenty-six Gd-EOB-MRI scans of patients undergoing chemotherapy for colorectal liver metastases (CRLM) were retrospectively analyzed. Three radiologists, blinded to clinical data, independently scored presence and severity of SOS on a 5-point scale (0, definitely not present to 4, definitely present). Patients with a score ≥3 were considered SOS+. Inter-observer agreement between readers was assessed with kappa statistics. Response (RECIST 1.1.), occurrence of new CRLM during follow-up (hepatic progression) and overall survival (OS) were compared between patients with and without SOS. RESULTS: The inter-observer agreement of SOS scores was poor, with quadratic kappas of 0.17-0.40. For the binary outcome of SOS+ (confidence level [CL] 3-4) vs. SOS- (CL 0-2) agreement was poor, with kappas of 0.03-0.37. Median follow-up was 24 months (range 4-44 months). Response and OS between patients with and without SOS did not differ significantly for any of the readers. CONCLUSION: Inter-observer agreement for the diagnosis of SOS on Gd-EOB-MRI is poor. No significant correlation with relevant outcomes was found for any of the readers. Therefore, MRI for SOS diagnosis might be less useful than previously reported. Other techniques should be explored to accurately diagnose SOS in absence of histological confirmation.