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Simple Summary: The differential diagnosis between uterine leiomyosarcomas and leiomyomas based on imaging represents one of the major challenges for gynecologists and radiologists. Currently, only histological examination can definitively resolve doubts in suspicious cases. The purpose of this study is to develop a machine learning model that can support clinical decision making. One of the proposed approaches, i.e., using the general linear model (GLM) classifier, has been patented by our team and has demonstrated good performance in retrospective analyses, with predicted area under the curve (AUC), sensitivity, and specificity on the test set ranging from 0.78 to 0.82, from 0.78 to 0.89, and from 0.67 to 0.87, respectively. The next step will involve validation at other medical centers and its prospective application. Background: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Methods: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. Results: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62–0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65–0.85). Conclusions: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions. [ABSTRACT FROM AUTHOR]

Background: High‐dose rate brachytherapy using a non‐sealed 188Rhenium resin (188Re) is a recently approved treatment option for non‐melanoma skin cancer (NMSC). The treatment goal is to deliver a personalized absorbed dose to the deepest point of neoplastic infiltration corresponding to the minimal target dose. The treatment consists of the application of a 188Re‐based resin over a plastic foil placed on the target skin surface. However, there is no treatment planning tool to assess the 188Re activity needed for a personalized treatment. Purpose: The paper aims to present a novel Monte Carlo (MC)‐based tool for 188Re‐based resin activity and dose calculation, experimentally validated using Gafchromic EBT3 films. Methods: MC simulations were carried out using FLUKA modeling density and composition of 188Re resin. The MC‐based look up table (LUT) was incorporated in an ad hoc developed tool. The proposed tool allows the personalized calculation of treatment parameters (i.e., activity to be dispensed, the treatment duration, and dose volume histograms), according to the target dimension. The proposed tool was compared using Bland–Altman analysis to the previous calculation approaches conducted using VARSKIN in a retrospective cohort of 76 patients. The tool was validated in ad hoc experimental set ups using a stack of calibrated Gafchromic EBT3 films covered by a plastic film and exposed using a homogenous activity distribution of 188Re eluate and a heterogeneous activity distribution of 188Re resin mimic the patient treatment. Results: The agreement between the proposed tool and VARSKIN was evaluated on the investigated cohort with median range of target area, target depth, and treatment time equal to 4.8 [1.0–60.1] cm2, 1.1 [0.2–3.0] mm, and 70 [21–285] min, with a median range of target dose (Gy) of 23.5 [10–54.9]. The calculated minimal target doses, ranged from 1% to 10% for intermediate target depths (1.2 ± 0.7 mm), while showing significant differences in the estimation of superficial (maximal) target doses. The agreement between MC calculation and measurements at different plans in a stack of Gafchromic EBT3 films was within 10% for both the homogenous and heterogeneous activity distribution of 188Re. Worst agreements were observed for absorbed doses lower than 0.3 Gy. Conclusions: Our results support the implementation of our MC‐based tool in the practical routine for calculating the 188Re resin activity and treatment parameters necessary for obtaining the prescribed minimal target dose. [ABSTRACT FROM AUTHOR]

Background: Transarterial radioembolization (TARE) relies on directly injected 90Y- or 166Ho-loaded microspheres in the hepatic arteries. The activity to be injected is generally based on pre-TARE 99mTc-macro-aggregated-albumin (MAA) imaging, while the actual dose distribution is based on post-treatment images. The volume of interest (VOIs) propagation methods (i.e., rigid and deformable) from pre- to post-TARE imaging might affect the estimation of the mean absorbed dose in the tumor and non-tumoral liver (NTL), i.e., DT and DNTL, respectively. Methods: In 101 consecutive patients, liver and tumor were delineated on pre-TARE images and semi-automatically transferred on 90Y-PET/CT images with a rigid or deformable registration approach. Pre- and post-TARE volumes and DT/DNTL/DL were compared using correlation coefficient (CC) indexes, such as intra-class (ICC), Pearson's (PCC), concordance (CCCo) and Bland–Altman analyses. The Kaplan–Meier curves of overall survival (OS) were calculated according to DT. Results: All computed CCs indicated very good (>0.92) agreement for volume comparison, while they suggested good (ICC ≥ 0.869, PCC ≥ 0.876 and CCCo ≥ 0.790) and moderate agreement in the intra- and inter-modality DT/DNTL/DL comparisons, respectively. Bland–Altman analyses showed percentage differences between the manual and deformable approaches of up to about −31%, 9% and 62% for tumoral volumes, DT and DNTL, respectively. The overall survival analysis showed statistically significant differences using DT cutoffs of 110, 90 and 85 Gy for the manual, rigid and deformable approaches, respectively. Conclusions: The semi-automatic transfer of VOIs from pre- and post-TARE imaging is feasible, but the selected method might affect prognostic DT/DNTL constraints. [ABSTRACT FROM AUTHOR]