COLOX: a new blood-based test for colorectal cancer (CRC)screening

BACKGROUND: The objective is to develop a cost-effective, reliable and non invasive screening test able to detect early CRCs and adenomas. This is done on a nucleic acids multigene assay performed on peripheral blood mononuclear cells (PBMCs). METHODS: A colonoscopy-controlled study was conducted on 179 subjects. 92 subjects (21 CRC, 30 adenoma >1 cm and 41 controls) were used as training set to generate a signature. Other 48 subjects kept blinded (controls, CRC and polyps) were used as a test set. To determine organ and disease specificity 38 subjects were used: 24 with inflammatory bowel disease (IBD),14 with other cancers (OC). Blood samples were taken and PBMCs were purified. After the RNA extraction, multiplex RT-qPCR was applied on 92 different candidate biomarkers. After different univariate and multivariate analysis 60 biomarkers with significant p-values (<0.01) were selected. 2 distinct biomarker signatures are used to separate patients without lesion from those with CRC or with adenoma, named COLOX CRC and COLOX POL. COLOX performances were validated using random resampling method, bootstrap. RESULTS: COLOX CRC and POL tests successfully separate patients without lesions from those with CRC (Se 67%, Sp 93%, AUC 0.87), and from those with adenoma > 1cm (Se 63%, Sp 83%, AUC 0.77). 6/24 patients in the IBD group and 1/14 patients in the OC group have a positive COLOX CRC. CONCLUSION: The two COLOX tests demonstrated a high Se and Sp to detect the presence of CRCs and adenomas > 1 cm. A prospective, multicenter, pivotal study is underway in order to confirm these promising results in a larger cohort

Dynamic contrast-enhanced MRI perfusion quantification in hepatocellular carcinoma: comparison of gadoxetate disodium and gadobenate dimeglumine

Objectives: (1) To assess the quality of the arterial input function (AIF) during dynamic contrast-enhanced (DCE) MRI of the liver and (2) to quantify perfusion parameters of hepatocellular carcinoma (HCC) and liver parenchyma during the first 3 min post-contrast injection with DCE-MRI using gadoxetate disodium compared to gadobenate dimeglumine (Gd-BOPTA) in different patient populations. Methods: In this prospective study, we evaluated 66 patients with 83 HCCs who underwent DCE-MRI, using gadoxetate disodium (group 1, n = 28) or Gd-BOPTA (group 2, n = 38). AIF qualitative and quantitative features were assessed. Perfusion parameters (based on the initial 3 min post-contrast) were extracted in tumours and liver parenchyma, including model-free parameters (time-to-peak enhancement (TTP), time-to-washout) and modelled parameters (arterial flow (Fa), portal venous flow (Fp), total flow (Ft), arterial fraction, mean transit time (MTT), distribution volume (DV)). In addition, lesion-to-liver contrast ratios (LLCRs) were measured. Fisher's exact tests and Mann-Whitney U tests were used to compare the two groups. Results: AIF quality, modelled and model-free perfusion parameters in HCC were similar between the 2 groups (p = 0.054-0.932). Liver parenchymal flow was lower and liver enhancement occurred later in group 1 vs group 2 (Fp, p = 0.002; Ft, p = 0.001; TTP, MTT, all p < 0.001), while there were no significant differences in tumour LLCR (max. positive LLCR, p = 0.230; max. negative LLCR, p = 0.317). Conclusion: Gadoxetate disodium provides comparable AIF quality and HCC perfusion parameters compared to Gd-BOPTA during dynamic phases. Despite delayed and decreased liver enhancement with gadoxetate disodium, LLCRs were equivalent between contrast agents, indicating similar tumour conspicuity. Key points: • Arterial input function quality, modelled, and model-free dynamic parameters measured in hepatocellular carcinoma are similar in patients receiving gadoxetate disodium or gadobenate dimeglumine during the first 3 min post injection. • Gadoxetate disodium and gadobenate dimeglumine show similar lesion-to-liver contrast ratios during dynamic phases in patients with HCC. • There is lower portal and lower total hepatic flow and longer hepatic mean transit time and time-to-peak with gadoxetate disodium compared to gadobenate dimeglumine. Keywords: Carcinoma, hepatocellular; Gadobenate dimeglumine; Gadoxetate; Liver neoplasms; Magnetic resonance imaging

Gadoxetate-enhanced Abbreviated MRI for Hepatocellular Carcinoma Surveillance: Preliminary Experience

PurposeTo describe a single-center preliminary experience with gadoxetate disodium-enhanced abbreviated MRI for hepatocellular carcinoma (HCC) screening and surveillance in patients with cirrhosis or chronic hepatitis B virus (cHBV).Materials and methodsThis was a retrospective study of consecutive patients aged 18 years and older with cirrhosis or cHBV who underwent at least one gadoxetate-enhanced abbreviated MRI examination for HCC surveillance from 2014 through 2016. Examinations were interpreted prospectively by one of six abdominal radiologists for clinical care. Clinical, imaging, and other data were extracted from electronic medical records. Diagnostic adequacy was assessed in all patients. Diagnostic accuracy was assessed in the subset of patients who could be classified as having HCC or not having HCC on the basis of a composite reference standard.ResultsIn this study, 330 patients (93% with cirrhosis; 45% women; mean age, 59 years) underwent gadoxetate-enhanced abbreviated MRI. In the 330 patients, 311 (94.2%) baseline gadoxetate-enhanced abbreviated MRI examinations were diagnostically adequate. Of 141 (43%) of the 330 patients, 91.4% (129 of 141) could be classified as not having HCC and 8.6% (12 of 141) could be classified as having HCC. Baseline gadoxetate-enhanced abbreviated MRI had 0.92 sensitivity (95% confidence interval [CI]: 0.62, 1.00) and 0.91 specificity (95% CI: 0.84, 0.95) for detection of HCC. Of the 330 patients who underwent baseline gadoxetate-enhanced abbreviated MRI, 187 (57%) were lost to follow-up.ConclusionGadoxetate-enhanced abbreviated MRI is feasible clinically, has a high diagnostic adequacy rate, and, on the basis of our preliminary experience, accurately depicts HCC in high-risk patients. Strategies to enhance follow-up compliance are needed.© RSNA, 2019Keywords: Abdomen/GI, Cirrhosis, Liver, MR-Imaging, Oncology, ScreeningSupplemental material is available for this article

Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks

OBJECTIVE To assess the performance of convolutional neural networks (CNNs) for semiautomated segmentation of hepatocellular carcinoma (HCC) tumors on MRI. METHODS This retrospective single-center study included 292 patients (237 M/55F, mean age 61 years) with pathologically confirmed HCC between 08/2015 and 06/2019 and who underwent MRI before surgery. The dataset was randomly divided into training (n = 195), validation (n = 66), and test sets (n = 31). Volumes of interest (VOIs) were manually placed on index lesions by 3 independent radiologists on different sequences (T2-weighted imaging [WI], T1WI pre-and post-contrast on arterial [AP], portal venous [PVP], delayed [DP, 3 min post-contrast] and hepatobiliary phases [HBP, when using gadoxetate], and diffusion-weighted imaging [DWI]). Manual segmentation was used as ground truth to train and validate a CNN-based pipeline. For semiautomated segmentation of tumors, we selected a random pixel inside the VOI, and the CNN provided two outputs: single slice and volumetric outputs. Segmentation performance and inter-observer agreement were analyzed using the 3D Dice similarity coefficient (DSC). RESULTS A total of 261 HCCs were segmented on the training/validation sets, and 31 on the test set. The median lesion size was 3.0 cm (IQR 2.0-5.2 cm). Mean DSC (test set) varied depending on the MRI sequence with a range between 0.442 (ADC) and 0.778 (high b-value DWI) for single-slice segmentation; and between 0.305 (ADC) and 0.667 (T1WI pre) for volumetric-segmentation. Comparison between the two models showed better performance in single-slice segmentation, with statistical significance on T2WI, T1WI-PVP, DWI, and ADC. Inter-observer reproducibility of segmentation analysis showed a mean DSC of 0.71 in lesions between 1 and 2 cm, 0.85 in lesions between 2 and 5 cm, and 0.82 in lesions > 5 cm. CONCLUSION CNN models have fair to good performance for semiautomated HCC segmentation, depending on the sequence and tumor size, with better performance for the single-slice approach. Refinement of volumetric approaches is needed in future studies. KEY POINTS • Semiautomated single-slice and volumetric segmentation using convolutional neural networks (CNNs) models provided fair to good performance for hepatocellular carcinoma segmentation on MRI. • CNN models' performance for HCC segmentation accuracy depends on the MRI sequence and tumor size, with the best results on diffusion-weighted imaging and T1-weighted imaging pre-contrast, and for larger lesions

Modeling tumor size dynamics based on real‐world electronic health records and image data in advanced melanoma patients receiving immunotherapy

Abstract The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model‐informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real‐world setting. We developed a tumor growth inhibition model based on real‐world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image‐based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high‐dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates