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Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.

Abstract Background Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. Methods Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. Results Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. Conclusion Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

Objectives: Evaluate the prevalence of genetic factors in a large population of infertile subjects and define the seminological, hormonal, and ultrasonographic features for each alteration. Methods: This single-center retrospective study included male partners of infertile couples undergoing genetic investigations due to oligozoospermia or azoospermia evaluated from January 2012 to January 2022. The genetic investigations consist of karyotype, CFTR gene mutations plus variant of the IVS8-5T polymorphic trait, Y chromosome microdeletion, and Next Generation Sequencing panel to analyze genes implicated in congenital hypogonadotropic hypogonadism (CHH). Results: Overall, 15.4% (72/466) of patients received a diagnosis of genetic cause of infertility. Specifically, 23 patients (31.9%) harbor mutations in the CFTR gene, 22 (30.6%) have a 47, XXY karyotype, 14 (19.4%) patients show a Y chromosome microdeletion, 7 (9.7%) have structural chromosomal anomalies, and 6 (8.3%) have CHH. Overall, 80.6% of patients were azoospermic and 19.4% oligozoospermic (sperm concentration 3.5 ± 3.8 million/mL). Almost all patients presented hormonal alterations related to the specific genotype, while the main ultrasound alterations were testicular hypoplasia, calcifications/microcalcifications, and enlarged/hyperechoic epididymis. Conclusions: The prevalence of genetic abnormalities in males of infertile couples was 15.4% in our Center. CFTR gene disease-causing variants resulted in more frequent, with various clinical features, highlighting the complexity and heterogeneity of the presentation. Other investigations are needed to understand if conditions like ring chromosomes and other translocations are related to infertility or are incidental factors. [ABSTRACT FROM AUTHOR]

Abstract Background Radiomics is expected to improve the management of metastatic colorectal cancer (CRC). We aimed at evaluating the impact of liver lesion contouring as a source of variability on radiomic features (RFs). Methods After Ethics Committee approval, 70 liver metastases in 17 CRC patients were segmented on contrast-enhanced computed tomography scans by two residents and checked by experienced radiologists. RFs from grey level co-occurrence and run length matrices were extracted from three-dimensional (3D) regions of interest (ROIs) and the largest two-dimensional (2D) ROIs. Inter-reader variability was evaluated with Dice coefficient and Hausdorff distance, whilst its impact on RFs was assessed using mean relative change (MRC) and intraclass correlation coefficient (ICC). For the main lesion of each patient, one reader also segmented a circular ROI on the same image used for the 2D ROI. Results The best inter-reader contouring agreement was observed for 2D ROIs according to both Dice coefficient (median 0.85, interquartile range 0.78–0.89) and Hausdorff distance (0.21 mm, 0.14–0.31 mm). Comparing RF values, MRC ranged 0–752% for 2D and 0–1567% for 3D. For 24/32 RFs (75%), MRC was lower for 2D than for 3D. An ICC > 0.90 was observed for more RFs for 2D (53%) than for 3D (34%). Only 2/32 RFs (6%) showed a variability between 2D and circular ROIs higher than inter-reader variability. Conclusions A 2D contouring approach may help mitigate overall inter-reader variability, albeit stable RFs can be extracted from both 3D and 2D segmentations of CRC liver metastases.

In the last years, the widespread use of the prostate-specific antigen (PSA) blood examination to triage patients who will enter the diagnostic/therapeutic path for prostate cancer (PCa) has almost halved PCa-specific mortality. As a counterpart, millions of men with clinically insignificant cancer not destined to cause death are treated, with no beneficial impact on overall survival. Therefore, there is a compelling need to develop tools that can help in stratifying patients according to their risk, to support physicians in the selection of the most appropriate treatment option for each individual patient. The aim of this study was to develop and validate on multivendor data a fully automated computer-aided diagnosis (CAD) system to detect and characterize PCas according to their aggressiveness. We propose a CAD system based on artificial intelligence algorithms that a) registers all images coming from different MRI sequences, b) provides candidates suspicious to be tumor, and c) provides an aggressiveness score of each candidate based on the results of a support vector machine classifier fed with radiomics features. The dataset was composed of 131 patients (149 tumors) from two different institutions that were divided in a training set, a narrow validation set, and an external validation set. The algorithm reached an area under the receiver operating characteristic (ROC) curve in distinguishing between low and high aggressive tumors of 0.96 and 0.81 on the training and validation sets, respectively. Moreover, when the output of the classifier was divided into three classes of risk, i.e., indolent, indeterminate, and aggressive, our method did not classify any aggressive tumor as indolent, meaning that, according to our score, all aggressive tumors would undergo treatment or further investigations. Our CAD performance is superior to that of previous studies and overcomes some of their limitations, such as the need to perform manual segmentation of the tumor or the fact that analysis is limited to single-center datasets. The results of this study are promising and could pave the way to a prediction tool for personalized decision making in patients harboring PCa.

Post‐bariatric hypoglycaemia (PBH) is a metabolic complication of bariatric surgery (BS), consisting of low post‐prandial glucose levels in patients having undergone bariatric procedures. While BS is currently the most effective and relatively safe treatment for obesity and its complications, the development of PBH can significantly impact patients' quality of life and mental health. The diagnosis of PBH is still challenging, considering the lack of definitive and reliable diagnostic tools, and the fact that this condition is frequently asymptomatic. However, PBH's prevalence is alarming, involving up to 88% of the post‐bariatric population, depending on the diagnostic tool, and this may be underestimated. Given the prevalence of obesity soaring, and an increasing number of bariatric procedures being performed, it is crucial that physicians are skilled to diagnose PBH and promptly treat patients suffering from it. While the milestone of managing this condition is nutritional therapy, growing evidence suggests that old and new pharmacological approaches may be adopted as adjunct therapies for managing this complex condition. [ABSTRACT FROM AUTHOR]

Introduction: The aims of this study were to determine the prevalence of testicular-adrenal rest tumors (T-ARTs) in patients with congenital adrenal hyperplasia (CAH) and to evaluate the related ultrasound (US) features, hormonal profiles, and semen parameters. Therefore, we attempted to understand the potential impact of adrenocorticotropic hormone (ACTH) on the persistence or disappearance of T-ART.Methods: We conducted a longitudinal cohort study including patients with CAH who were undergoing treatment with cortisone and, when indicated, fludrocortisone replacement therapy. We performed andrological examinations, US of the testis, hormone profiling, and semen analysis.Results: Of the 25 patients (mean ± SD age, 32.2 ± 7.5 years), T-ARTs were detected by US in 14 (56.0%) patients. The mean ± SD diameter of the lesions was 13.2 ± 6.8 mm. Among 3 (21.4%) patients, the lesions were observed in one testis, whereas both testes were affected in the remaining 11 (78.6%) patients. The lesions were hypoechoic in 12 (85.7%) patients and hyperechoic in 2 (14.3%). Plasma ACTH and 17-hydroxyprogesterone (17-OHP) levels were significantly higher in patients with T-ART than in patients without lesions (319.4 ± 307.0 pg/ml and 12.4 ± 2.7 ng/ml vs. 33.5 ± 10.7 pg/ml and 8.2 ± 1.8 ng/ml, respectively; p < 0.01). The mean values of sperm concentration and motility were significantly lower in patients with T-ART than in patients without lesions (12.1 ± 12.4 × 106 cells/ml and 18.4 ± 11.1% vs. 41.5 ± 23.2 × 106 cells/ml and 30.8 ± 15.4%, respectively; p < 0.05). Logistic regression analysis showed ACTH level as a significant predictor of T-ART (p < 0.05). In patients with T-ART, the dose of hydrocortisone was increased by ~25–30%, while the fludrocortisone treatment remained unchanged. After 6 months of steroid treatment, patients underwent US and hormonal evaluation. Disappearance and a reduction in T-ART were observed in 6 (42.9%) and 5 (35.7%) patients, respectively; a reduction in ACTH levels (from 319.4 ± 307.0 to 48.1 ± 5.1 pg/ml; p < 0.01) was reported. A significant correlation between ACTH level reduction and T-ART diameter reduction was observed (p < 0.5; r = 0.55).Conclusions: T-ARTs were detected in 56% of patients with CAH and were associated with impaired semen parameters. However, these lesions are potentially reversible, as demonstrated by the disappearance/reduction after adjustment of cortisone therapy and by the reduction in plasma ACTH level. Our study supports the importance of periodic US evaluation and maintenance of plasma ACTH levels within the normal range in men with CAH.

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.