Prognostic Value of Whole-Body PET Volumetric Parameters Extracted from 68Ga-DOTATOC PET/CT in Well-Differentiated Neuroendocrine Tumors.

Our objective was to evaluate the prognostic value of somatostatin receptor tumor burden on 68Ga-DOTATOC PET/CT in patients with well-differentiated (WD) neuroendocrine tumors (NETs). Methods: We retrospectively analyzed the 68Ga-DOTATOC PET/CT scans of 84 patients with histologically confirmed WD NETs (51 grade 1, 30 grade 2, and 3 grade 3). For each PET/CT scan, all 68Ga-DOTATOC-avid lesions were independently segmented by 2 operators using a customized threshold based on the healthy liver SUVmax (LIFEx, version 5.1). Somatostatin receptor-expressing tumor volume (SRETV) and total lesion somatostatin receptor expression (TLSRE = SRETV × SUVmean) were extracted for each lesion, and then whole-body SRETV and TLSRE (SRETVwb and TLSREwb, respectively) were defined as the sum of SRETV and TLSRE, respectively, for all segmented lesions in each patient. Time to progression (TTP) was defined as the combination of disease-free survival in patients undergoing curative surgery (n = 10) and progression-free survival for patients with unresectable or metastatic disease (n = 74). TTP and overall survival were calculated by Kaplan-Meier analysis, log-rank testing, and the Cox proportional-hazards regression model. Results: After a median follow-up of 15.5 mo, disease progression was confirmed in 35 patients (41.7%) and 14 patients died. A higher SRETVwb (>39.1 cm3) and TLSREwb (>306.8 g) correlated significantly with a shorter median TTP (12 mo vs. not reached; P < 0.001). In multivariate analysis, SRETVwb (P = 0.005) was the only independent predictor of TTP regardless of histopathologic grade and TNM staging. Conclusion: According to our results, SRETVwb and TLSREwb extracted from 68Ga-DOTATOC PET/CT could predict TTP or overall survival and might have important clinical utility in the management of patients with WD NETs.

Automatic segmentation of ultrasound images of gastrocnemius medialis with different echogenicity levels using convolutional neural networks.

The purpose of this study was to develop an automatic method for the segmentation of muscle cross-sectional area on transverse B-mode ultrasound images of gastrocnemius medialis using a convolutional neural network(CNN). In the provided dataset images with both normal and increased echogenicity are present. The manually annotated dataset consisted of 591 images, from 200 subjects, 400 relative to subjects with normal echogenicity and 191 to subjects with augmented echogenicity. From the DICOM files, the image has been extracted and processed using the CNN, then the output has been post-processed to obtain a finer segmentation. Final results have been compared to the manual segmentations. Precision and Recall scores as mean ± standard deviation for training, validation, and test sets are 0.96 ± 0.05, 0.90 ± 0.18, 0.89 ± 0.15 and 0.97 ±0.03, 0.89± 0.17, 0.90 ± 0.14 respectively. The CNN approach has also been compared to another automatic algorithm, showing better performances. The proposed automatic method provides an accurate estimation of muscle cross-sectional area in muscles with different echogenicity levels.

Multimodal T2w and DWI Prostate Gland Automated Registration.

Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre- and post- registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI.

Automatic Extraction of Dermatological Parameters from Nevi Using an Inexpensive Smartphone Microscope: A Proof of Concept.

The evolution of smartphone technology has made their use more common in dermatological applications. Here we studied the feasibility of using an inexpensive smartphone microscope for the extraction of dermatological parameters and compared the results obtained with a portable dermoscope, commonly used in clinical practice. Forty-two skin lesions were imaged with both devices and visually analyzed by an expert dermatologist. The presence of a reticular pattern was observed in 22 dermoscopic images, but only in 10 smartphone images. The proposed paradigm segments the image and extracts texture features which are used to train and validate a neural network to classify the presence of a reticular pattern. Using 5-fold cross-validation, an accuracy of 100% and 95% was obtained with the dermoscopic and smartphone images, respectively. This approach can be useful for general practitioners and as a triage tool for skin lesion analysis.