Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation.

OBJECTIVES: Independent internal and external validation of three previously published CT-based radiomics models to predict local tumor progression (LTP) after thermal ablation of colorectal liver metastases (CRLM). MATERIALS AND METHODS: Patients with CRLM treated with thermal ablation were collected from two institutions to collect a new independent internal and external validation cohort. Ablation zones (AZ) were delineated on portal venous phase CT 2-8 weeks post-ablation. Radiomics features were extracted from the AZ and a 10 mm peri-ablational rim (PAR) of liver parenchyma around the AZ. Three previously published prediction models (clinical, radiomics, combined) were tested without retraining. LTP was defined as new tumor foci appearing next to the AZ up to 24 months post-ablation. RESULTS: The internal cohort included 39 patients with 68 CRLM and the external cohort 52 patients with 78 CRLM. 34/146 CRLM developed LTP after a median follow-up of 24 months (range 5-139). The median time to LTP was 8 months (range 2-22). The combined clinical-radiomics model yielded a c-statistic of 0.47 (95%CI 0.30-0.64) in the internal cohort and 0.50 (95%CI 0.38-0.62) in the external cohort, compared to 0.78 (95%CI 0.65-0.87) in the previously published original cohort. The radiomics model yielded c-statistics of 0.46 (95%CI 0.29-0.63) and 0.39 (95%CI 0.28-0.52), and the clinical model 0.51 (95%CI 0.34-0.68) and 0.51 (95%CI 0.39-0.63) in the internal and external cohort, respectively. CONCLUSION: The previously published results for prediction of LTP after thermal ablation of CRLM using clinical and radiomics models were not reproducible in independent internal and external validation. CLINICAL RELEVANCE STATEMENT: Local tumour progression after thermal ablation of CRLM cannot yet be predicted with the use of CT radiomics of the ablation zone and peri-ablational rim. These results underline the importance of validation of radiomics results to test for reproducibility in independent cohorts. KEY POINTS: • Previous research suggests CT radiomics models have the potential to predict local tumour progression after thermal ablation in colorectal liver metastases, but independent validation is lacking. • In internal and external validation, the previously published models were not able to predict local tumour progression after ablation. • Radiomics prediction models should be investigated in independent validation cohorts to check for reproducibility.

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.

GEP-NET radiomics: a systematic review and radiomics quality score assessment.

OBJECTIVE: The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence. METHODS: PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data. RESULTS: In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%. CONCLUSION: Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce. KEY POINTS: • The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.

CT-Based Radiomics Analysis Before Thermal Ablation to Predict Local Tumor Progression for Colorectal Liver Metastases.

PURPOSE: Predicting early local tumor progression after thermal ablation treatment for colorectal liver metastases patients is critical for the decision of subsequent follow-up and treatment. Radiomics features derived from medical images show great potential for prediction and prognosis. The aim is to develop and validate a machine learning radiomics model to predict local tumor progression based on the pre-ablation CT scan of colorectal liver metastases patients. MATERIALS AND METHODS: Ninety patients with colorectal liver metastases (140 lesions) treated by ablation were included in the study and were randomly divided into a training (n = 63 patients/n = 94 lesions) and validation (n = 27 patients/n = 46 lesions) cohort. After manual lesion volume segmentation and preprocessing, 1593 radiomics features were extracted for each lesion. Three machine learning survival models were constructed based on (1) radiomics features, (2) clinical features and (3) a combination of clinical and radiomics features to predict local tumor progression free survival. Feature reduction and machine learning modeling were performed and optimized with sequential model-based optimization. RESULTS: Median follow-up was 24 months (range 6-115). Thirty-one (22%) lesions developed local tumor progression. The concordance index in the validation set to predict local tumor progression free survival was 0.78 (95% confidence interval [CI]: 0.77-0.79) for the radiomics model, 0.56 (95%CI: 0.55-0.57) for the clinical model and 0.79 (95%CI: 0.78-0.80) for the combined model. CONCLUSION: A machine learning-based radiomics analysis of routine clinical CT imaging pre-ablation could act as a valuable biomarker model to predict local tumor progression with curative intent for colorectal liver metastases patients.

Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review.

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on 18F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.

Blood Molecular Genomic Analysis Predicts the Disease Course of Gastroenteropancreatic Neuroendocrine Tumor Patients: A Validation Study of the Predictive Value of the NETest®.

Reliable prediction of disease status is a major challenge in managing gastroenteropancreatic neuroendocrine tumors (GEP-NETs). The aim of the study was to validate the NETest®, a blood molecular genomic analysis, for predicting the course of disease in individual patients compared to chromogranin A (CgA). NETest® score (normal ≤20%) and CgA level (normal <100 µg/L) were measured in 152 GEP-NETs. The median follow-up was 36 (4-56) months. Progression-free survival was blindly assessed (Response Evaluation Criteria in Solid Tumors [RECIST] version 1.1). Optimal cutoffs (area under the receiver operating characteristic curve [AUC]), odds ratios, as well as negative and positive predictive values (NPVs/PPVs) were calculated for predicting stable disease (SD) and progressive disease (PD). Of the 152 GEP-NETs, 86% were NETest®-positive and 52% CgA-positive. -NETest® AUC was 0.78 versus CgA 0.73 (p = ns). The optimal cutoffs for predicting SD/PD were 33% for the NETest® and 140 µg/L for CgA. Multivariate analyses identified NETest® as the strongest predictor for PD (odds ratio: 5.7 [score: 34-79%]; 12.6 [score: ≥80%]) compared to CgA (odds ratio: 3.0), tumor grade (odds ratio: 3.1), or liver metastasis (odds ratio: 7.7). The NETest® NPV for SD was 87% at 12 months. The PPV for PD was 47 and 64% (scores 34-79% and ≥80%, respectively). NETest® metrics were comparable in the watchful waiting, treatment, and no evidence of disease (NED) subgroups. For CgA (>140 ng/mL), NPV and PPV were 83 and 52%. CgA could not predict PD in the watchful waiting or NED subgroups. The NETest® reliably predicted SD and was the strongest predictor of PD. CgA had lower utility. The -NETest® anticipates RECIST-defined disease status up to 1 year before imaging alterations are apparent.

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.

Patient-Specific Implant for Residual Facial Asymmetry following Orthognathic Surgery in Unilateral Craniofacial Microsomia.

Craniofacial microsomia (CFM) is a congenital anomaly with a variable phenotype. The most prominent feature of CFM is a predominantly unilateral hypoplasia of the mandible, leading to facial asymmetry. Even after correction of the midline, there is often a remaining hard- and soft-tissue deficiency over the body of the mandible and cheek on the affected side. This clinical report describes the skeletal augmentation of the mandible with a patient-specific implant to treat residual facial asymmetry in two female patients with unilateral CFM. Good aesthetic results were achieved in both patients treated with either a Medpor or polyetheretherketone implant without complications after a follow-up time of 55 and 30 months, respectively.

Describing Crouzon and Pfeiffer syndrome based on principal component analysis.

UNLABELLED: Crouzon and Pfeiffer syndrome are syndromic craniosynostosis caused by specific mutations in the FGFR genes. Patients share the characteristics of a tall, flattened forehead, exorbitism, hypertelorism, maxillary hypoplasia and mandibular prognathism. Geometric morphometrics allows the identification of the global shape changes within and between the normal and syndromic population. METHODS: Data from 27 Crouzon-Pfeiffer and 33 normal subjects were landmarked in order to compare both populations. With principal component analysis the variation within both groups was visualized and the vector of change was calculated. This model normalized a Crouzon-Pfeiffer skull and was compared to age-matched normative control data. RESULTS: PCA defined a vector that described the shape changes between both populations. Movies showed how the normal skull transformed into a Crouzon-Pfeiffer phenotype and vice versa. Comparing these results to established age-matched normal control data confirmed that our model could normalize a Crouzon-Pfeiffer skull. CONCLUSIONS: PCA was able to describe deformities associated with Crouzon-Pfeiffer syndrome and is a promising method to analyse variability in syndromic craniosynostosis. The virtual normalization of a Crouzon-Pfeiffer skull is useful to delineate the phenotypic changes required for correction, can help surgeons plan reconstructive surgery and is a potentially promising surgical outcome measure.