RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. We compare the method to the Eclipse contouring software and find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods, with hearts that take 2 minutes and 2 seconds to delineate on average, after 923 images have been delineated, compared to 7 minutes and 1 seconds when delineating manually. Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows. Source code is available at \href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}

(68)Ga-DOTATOC PET and gene expression profile in patients with neuroendocrine carcinomas:strong correlation between PET tracer uptake and gene expression of somatostatin receptor subtype 2

Somatostatin receptor expression on both protein and gene expression level was compared with in vivo (68)Ga-DOTATOC PET/CT in patients with neuroendocrine carcinomas (NEC). Twenty-one patients with verified NEC who underwent a (68)Ga-DOTATOC PET/CT between November 2012 and May 2014, were retrospectively included. By real-time polymerase chain reaction, we quantitatively determined the gene expression of several genes and compared with (68)Ga-DOTATOC PET uptake. By immunohistochemistry we qualitatively studied the expression of assorted proteins in NEC. The median age at diagnosis was 68 years (range 41-84) years. All patients had WHO performance status 0-1. Median Ki67 index was 50% (range 20-100%). Gene expression of somatostatin receptor subtype (SSTR) 2 and Ki67 were both positively correlated to the (68)Ga-DOTATOC uptake (r=0.89; p<0.0001 and r=0.5; p=0.021, respectively). Furthermore, SSTR2 and SSTR5 gene expression were strongly and positively correlated (r=0.57; p=0.006). This study as the first verifies a positive and close correlation of (68)Ga-DOTATOC uptake and gene expression of SSTR2 in NEC. SSTR2 gene expression has a stronger correlation to (68)Ga-DOTATOC uptake than SSTR5. In addition, the results indicate that the gene expression levels of SSTR2 and SSTR5 at large follow one another

Angiosarcoma of the Scalp: Metastatic Pulmonary Cystic Lesions Initially Misinterpreted as Benign Findings on 18F-FDG PET/CT

Angiosarcomas are rare and only represent about 2% of all soft tissue sarcomas. They arise from vascular or lymphatic endothelial cells and are most commonly located in the heart, liver, breast, and skin. Cutaneous angiosarcoma of the scalp is highly malignant and with dismal prognosis. Reported five-year survival is &lt;30%. The mainstay of treatment is surgical resection and adjuvant radiation therapy, but failure rates following local therapy are high. Cutaneous angiosarcoma of the scalp has a predilection for pulmonary metastases with a variety of morphologic patterns on imaging. Metastatic disease in terms of pulmonary thin-walled, cystic lesions, may not be hypermetabolic on 18F-FDG PET and, as such, could be misinterpreted as benign findings. We present a case demonstrating the diagnostic uncertainty and delay in an elderly male with angiosarcoma of the scalp presenting with metastatic lung lesions following failure of local therapy