Response to Letter: Intrapatient Comparison of the Hepatobiliary Phase of Gd-BOPTA and Gd-EOB-DTPA in the Differentiation of HCA From FNH

Level of Evidence: 5. Technical Efficacy Stage: 3

Detection of metastases using circulating tumour DNA in uveal melanoma

Background: Approximately 50% of uveal melanoma (UM) patients will develop metastatic disease depending on the genetic features of the primary tumour. Patients need 3–12 monthly scans, depending on their prognosis, which is costly and often non-specific. Circulating tumour DNA (ctDNA) quantification could serve as a test to detect and monitor patients for early signs of metastasis and therapeutic response. Methods: We assessed ctDNA as a biomarker in three distinct UM cohorts using droplet-digital PCR: (A) a retrospective analysis of primary UM patients to predict metastases; (B) a prospective analysis of UM patients after resolution of their primary tumour for early detection of metastases; and (C) monitoring treatment response in metastatic UM patients. Results: Cohort A: ctDNA levels were not associated with the development of metastases. Cohort B: ctDNA was detected in 17/25 (68%) with radiological diagnosis of metastases. ctDNA was the strongest predictor of overall survival in a multivariate analysis (HR = 15.8, 95% CI 1.7–151.2, p = 0.017). Cohort C: ctDNA monitoring of patients undergoing immunotherapy revealed a reduction in the levels of ctDNA in patients with combination immunotherapy. Conclusions: Our proof-of-concept study shows the biomarker feasibility potential of ctDNA monitoring in for the clinical management of uveal melanoma patients

¹⁸F-FDG PET/MRI for restaging esophageal cancer after neoadjuvant chemoradiotherapy

PURPOSE: The purpose of this study was to investigate whether 18F-fluorodeoxyglucose ( 18 F-FDG) PET/MRI may potentially improve tumor detection after neoadjuvant chemoradiotherapy (nCRT) for esophageal cancer. METHODS: This was a prospective, single-center feasibility study. At 6-12 weeks after nCRT, patients underwent standard 18 F-FDG PET/computed tomography (CT) followed by PET/MRI, and completed a questionnaire to evaluate burden. Two teams of readers either assessed the 18 F-FDG PET/CT or the 18 F-FDG PET/MRI first; the other scan was assessed 1 month later. Maximum standardized uptake value corrected for lean body mass (SUL max ) and mean apparent diffusion coefficient (ADC mean ) were measured at the primary tumor location. Histopathology of the surgical resection specimen served as the reference standard for diagnostic accuracy calculations. When patients had a clinically complete response and continued active surveillance, response evaluations until 9 months after nCRT served as a proxy for ypT and ypN (i.e. 'ycT' and 'ycN'). RESULTS: In the 21 included patients [median age 70 (IQR 62-75), 16 males], disease recurrence was found in the primary tumor in 14 (67%) patients (of whom one ypM+, detected on both scans) and in locoregional lymph nodes in six patients (29%). Accuracy (team 1/team 2) to detect yp/ycT+ with 18 F-FDG PET/MRI vs. 18 F-FDG PET/CT was 38/57% vs. 76/61%. For ypN+, accuracy was 63/53% vs. 63/42%, resp. Neither SUL max (both scans) nor ADC mean were discriminatory for yp/ycT+ . Fourteen of 21 (67%) patients were willing to undergo a similar 18 F-FDG PET/MRI examination in the future. CONCLUSION: 18 F-FDG PET/MRI currently performs comparably to 18 F-FDG PET/CT. Improvements in the scanning protocol, increasing reader experience and performing serial scans might contribute to enhancing the accuracy of tumor detection after nCRT using 18 F-FDG PET/MRI. TRIAL REGISTRATION: Netherlands Trial Register NL9352.</p

¹⁸F-FDG PET/MRI for restaging esophageal cancer after neoadjuvant chemoradiotherapy

PURPOSE: The purpose of this study was to investigate whether 18F-fluorodeoxyglucose ( 18 F-FDG) PET/MRI may potentially improve tumor detection after neoadjuvant chemoradiotherapy (nCRT) for esophageal cancer. METHODS: This was a prospective, single-center feasibility study. At 6-12 weeks after nCRT, patients underwent standard 18 F-FDG PET/computed tomography (CT) followed by PET/MRI, and completed a questionnaire to evaluate burden. Two teams of readers either assessed the 18 F-FDG PET/CT or the 18 F-FDG PET/MRI first; the other scan was assessed 1 month later. Maximum standardized uptake value corrected for lean body mass (SUL max ) and mean apparent diffusion coefficient (ADC mean ) were measured at the primary tumor location. Histopathology of the surgical resection specimen served as the reference standard for diagnostic accuracy calculations. When patients had a clinically complete response and continued active surveillance, response evaluations until 9 months after nCRT served as a proxy for ypT and ypN (i.e. 'ycT' and 'ycN'). RESULTS: In the 21 included patients [median age 70 (IQR 62-75), 16 males], disease recurrence was found in the primary tumor in 14 (67%) patients (of whom one ypM+, detected on both scans) and in locoregional lymph nodes in six patients (29%). Accuracy (team 1/team 2) to detect yp/ycT+ with 18 F-FDG PET/MRI vs. 18 F-FDG PET/CT was 38/57% vs. 76/61%. For ypN+, accuracy was 63/53% vs. 63/42%, resp. Neither SUL max (both scans) nor ADC mean were discriminatory for yp/ycT+ . Fourteen of 21 (67%) patients were willing to undergo a similar 18 F-FDG PET/MRI examination in the future. CONCLUSION: 18 F-FDG PET/MRI currently performs comparably to 18 F-FDG PET/CT. Improvements in the scanning protocol, increasing reader experience and performing serial scans might contribute to enhancing the accuracy of tumor detection after nCRT using 18 F-FDG PET/MRI. TRIAL REGISTRATION: Netherlands Trial Register NL9352.</p

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

The CARTS study: Chemoradiation therapy for rectal cancer in the distal rectum followed by organ-sparing transanal endoscopic microsurgery

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