The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W) images, with and without fat saturation(WWFS), and dynamic-contrast-enhanced(DCE)-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images) using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG), fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard) were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3–7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images.

Background: Radiomics has been applied to predict recurrence in several disease sites, but current approaches are typically restricted to analyzing tumor features, neglecting non-tumor information in the rest of the body. The purpose of this work was to develop and validate a model incorporating non-tumor radiomics, including whole body features, to predict treatment outcomes in patients with previously untreated locoregionally advanced cervical cancer. Methods:We analyzed 127 cervical cancer patients treated definitively with chemoradiotherapy and intracavitary brachytherapy. All patients underwent pretreatment whole body 18 F-FDG PET/CT. To quantify effects due to the tumor itself, the gross tumor volume (GTV) was directly contoured on the PET/CT. Meanwhile, to quantify effects arising from the rest of the body, the planning target volume (PTV) was deformably registered from each planning CT to the PET/CT, and a semi-automated approach combining seed-growing and manual contour review generated whole body muscle, bone, and fat segmentations on each PET/CT. A total of 965 radiomic features were extracted for GTV, PTV, muscle, bone, and fat. 95 patients were used to train a Cox model of disease recurrence including both radiomic and clinical features (age, stage, tumor grade, histology, and baseline complete blood cell counts), using bagging and split-sample-validation for feature reduction and model selection. To further avoid overfitting, the resulting models were tested for generalization on the remaining 32 patients, by calculating a risk score based on Cox regression and evaluating the c-index (c-index > 0.5 indicates predictive power). Results: Optimal performance was seen in a Cox model including one clinical biomarker (whether or not a tumor was stage III-IVA), two GTV radiomic biomarkers (PET gray-level size-zone matrix small area low gray level emphasis and zone entropy), one PTV radiomic biomarker (major axis length) and one whole body radiomic biomarker (CT Bone root mean square). In particular, stratification into high-and lowrisk groups, based on the linear risk score from this Cox model, resulted in a hazard ratio [95% CI] of 0.019 [0.004, 0.082], an improvement over stratification based on clinical stage alone, which had a hazard ratio of 0.36 [0.16, 0.83]. Conclusions: Incorporating non-tumor radiomic biomarkers can improve the performance of prognostic models compared to using only clinical and tumor radiomic biomarkers. Future work should look to further test these models in larger, multiinstitutional cohorts.

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INTRODUCTION: Pulmonary arterial aneurysms (PAA) are a rare finding with most located proximally and are most commonly due to congenital heart disease and collagen vascular disease. We present a case of a patient presenting to interventional pulmonary clinic for biopsy of right sided mass seen on outside hospital CT found to be a large pulmonary arterial aneurysm. CASE PRESENTATION:A 68 y/o man presents to the interventional pulmonary clinic with a right lower lobe nodule incidentally noted on a CT A/P that measured 18mm. He has past medical history of hepatitis C, hypertension, hyperlipidemia, and prior abdominal surgeries for complicated diverticulitis. He has a smoking history of 45 pack years and quit about 9 years ago. His mother had lung cancer but otherwise has no personal history of cancer. He used to work as a welder and had exposure to asbestos. He takes medications for his hypertension and hyperlipidemia and takes aspirin for unclear reasons. His physical exam was unremarkable with normal vital signs and no clubbing, rashes, arthritis, or murmurs. An outside hospital radiology read of his CT A/P stated likely hamartoma vs carcinoid and less likely to be aneurysm. He underwent a PET scan showing the lesion with SUV max of 2.3 and a CT pulmonary artery protocol was performed which showed uniform contrast opacification of this lesion extending from a lobar PA confirming the diagnosis of a PA aneurysm. Interestingly this lesion was seen on a non-contrast CT of his chest done 20 years prior and given lack of change no interventions were deemed necessary at this time and close follow up was arranged.DISCUSSION: PAA is a rare entity and is usually a result of congenital heart disease. Acquired PAA can be due to rare collagen vascular diseases like Ehlers-Danlos syndrome and Marfan syndrome. Mycotic aneurysms can be caused by syphilis, tuberculosis, and many bacterium and fungi. Vasculitidies like behçet syndrome and Hugh-Stovin syndrome are also associated with PAA. Severe pulmonary artery hypertension can cause proximal PAA. Iatrogenic trauma to the pulmonary artery from PA catheters or surgeries is another cause. In our patient, given his age, stability, and lack of symptoms, we presumed his PAA was idiopathic or possibility a congenital defect and no further interventions were undertaken. Pseudoaneurysms or rapidly expanding aneurysms would need to be intervened upon due to risk of rupture, however given his stability this was deferred. CONCLUSIONS:It is important to be aware of pulmonary artery aneurysms as they can masquerade as a pulmonary mass and can sometimes be missed if IV contrast is not applied for a study. Secondary causes should be sought out and if the patient is symptomatic or having hemoptysis, coil embolization should be pursued.