A robust semi-automatic delineation workflow using denoised diffusion weighted magnetic resonance imaging for response assessment of patients with esophageal cancer treated with neoadjuvant chemoradiotherapy.

Background and Purpose: Diffusion weighted magnetic resonance imaging (DW-MRI) can be prognostic for response to neoadjuvant chemotherapy (nCRT) in patients with esophageal cancer. However, manual tumor delineation is labor intensive and subjective. Furthermore, noise in DW-MRI images will propagate into the corresponding apparent diffusion coefficient (ADC) signal. In this study a workflow is investigated that combines a denoising algorithm with semi-automatic segmentation for quantifying ADC changes. Materials and Methods: Twenty patients with esophageal cancer who underwent nCRT before esophagectomy were included. One baseline and five weekly DW-MRI scans were acquired for every patient during nCRT. A self-supervised learning denoising algorithm, Patch2Self, was used to denoise the DWI-MRI images. A semi-automatic delineation workflow (SADW) was next developed and compared with a manually adjusted workflow (MAW). The agreement between workflows was determined using the Dice coefficients and Brand Altman plots. The prognostic value of ADCmean increases (%/week) for pathologic complete response (pCR) was assessed using c-statistics. Results: The median Dice coefficient between the SADW and MAW was 0.64 (interquartile range 0.20). For the MAW, the c-statistic for predicting pCR was 0.80 (95% confidence interval (CI):0.56-1.00). The SADW showed a c-statistic of 0.84 (95%CI:0.63-1.00) after denoising. No statistically significant differences in c-statistics were observed between the workflows or after applying denoising. Conclusions: The SADW resulted in non-inferior prognostic value for pCR compared to the more laborious MAW, allowing broad scale applications. The effect of denoising on the prognostic value for pCR needs to be investigated in larger cohorts.

Challenges During Esophagectomy in Presence of Thoracic Anatomical Anomalies: A Report of Three Cases.

BACKGROUND/AIM: Esophagectomy for esophageal cancer is a complex surgical procedure with predefined surgical steps. Anatomical anomalies can offer additional challenges in case of surgical treatment. We present three cases of esophageal carcinoma with thoracic anatomical anomalies affecting the treatment strategy. The aim was to address the anatomical challenges of the anomalies and their impact on surgery as well as elaborate on possible solutions. CASE REPORT: We present three patient cases with anomalies of the thoracic anatomy. The first patient had a tumor with suspected ingrowth in an arteria lusoria. Restaging after definitive chemoradiotherapy showed progression of disease but without previously noted signs of vascular wall invasion and salvage robot-assisted minimally invasive esophagectomy (RAMIE) was performed. The second patient had an azygos lobe of the lung and underwent RAMIE after neoadjuvant chemoradiotherapy. The azygos vein was clipped, and paratracheal lymph node dissection was performed, however with limited extent at the right side due to the anatomical situation. The third patient was diagnosed with a right aortic arch, where the aortic arch transverses over the right bronchus instead of the left and descends to the right of and posterior to the esophagus and trachea. Treatment included definitive chemoradiotherapy (dCRT) without surgery, given the anatomical situation and a complete clinical response to dCRT of a squamous cell carcinoma. CONCLUSION: Thoracic anatomical anomalies are rare entities, which can be asymptomatic. Clinical implications can arise in combination with diagnosis of esophageal cancer and the need for surgical treatment. Therefore, detailed information regarding possible anomalies must be obtained prior to surgery and potential challenges have to be taken into consideration. Resection of related structures with tumor ingrowth can be considered in selected cases to achieve a radical resection.

The Predictive Value of Low Muscle Mass as Measured on CT Scans for Postoperative Complications and Mortality in Gastric Cancer Patients: A Systematic Review and Meta-Analysis.

Risk assessment is relevant to predict outcomes in patients with gastric cancer. This systematic review aimed to investigate the predictive value of low muscle mass for postoperative complications in gastric cancer patients. A systematic literature search was performed to identify all articles reporting on muscle mass as measured on computed tomography (CT) scans in patients with gastric cancer. After full text screening, 15 articles reporting on 4887 patients were included. Meta-analysis demonstrated that patients with low muscle mass had significantly higher odds of postoperative complications (odds ratio (OR): 2.09, 95% confidence interval (CI): 1.55-2.83) and severe postoperative complications (Clavien-Dindo grade ≥III, OR: 1.73, 95% CI: 1.14-2.63). Moreover, patients with low muscle mass had a significantly higher overall mortality (hazard ratio (HR): 1.81, 95% CI: 1.52-2.14) and disease-specific mortality (HR: 1.58, 95% CI: 1.36-1.84). In conclusion, assessment of muscle mass on CT scans is a potential relevant clinical tool for risk prediction in gastric cancer patients. Considering the heterogeneity in definitions applied for low muscle mass on CT scans in the included studies, a universal cutoff value of CT-based low muscle mass is required for more reliable conclusions.