Testicular Cancer Treatments and Sexuality: A Narrative Review.

The incidence of testicular cancer (TC) has been rapidly increasing over the past years. Diagnosis and early treatment have shown good oncological control, guaranteeing the patient different treatment approaches according to histology and tumor stage. Currently, physicians usually prioritize oncological outcomes over sexual outcomes and quality of life, considering as a first aim the overall survival of the patients; however, differently from other neoplasms, quality of life is still strongly affected among TC patients, and sexual outcomes are frequently compromised after each TC treatment. Several studies have suggested that each treatment approach may be associated with sexual dysfunctions, including erectile dysfunction, ejaculatory disorders, fertility issues, and hormonal changes. Since testicular cancer patients are more frequently young men, the subject of this work is substantial and should be analyzed in detail to help specialists in the management of this disease. The aim of the current narrative review is to generally describe every treatment for TC, including surgery, chemotherapy, radiotherapy, and retroperitoneal lymph node dissection, and to establish which sexual dysfunction may be specifically associated with each therapy.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.

BACKGROUND: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. METHODS: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. RESULTS: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values). CONCLUSIONS: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning.

Diagnosis based on histopathology for skin cancer detection is today's gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover the notion of atypia or dysplasia of the visible cells used for diagnosis is very subjective, with poor inter-rater agreement reported in the literature. Lastly, histology requires a biopsy which is an invasive procedure and only captures a small sample of the lesion, which is insufficient in the context of large fields of cancerization. Here we demonstrate that the notion of cellular atypia can be objectively defined and quantified with a non-invasive in-vivo approach in three dimensions (3D). A Deep Learning (DL) algorithm is trained to segment keratinocyte (KC) nuclei from Line-field Confocal Optical Coherence Tomography (LC-OCT) 3D images. Based on these segmentations, a series of quantitative, reproducible and biologically relevant metrics is derived to describe KC nuclei individually. We show that, using those metrics, simple and more complex definitions of atypia can be derived to discriminate between healthy and pathological skins, achieving Area Under the ROC Curve (AUC) scores superior than 0.965, largely outperforming medical experts on the same task with an AUC of 0.766. All together, our approach and findings open the door to a precise quantitative monitoring of skin lesions and treatments, offering a promising non-invasive tool for clinical studies to demonstrate the effects of a treatment and for clinicians to assess the severity of a lesion and follow the evolution of pre-cancerous lesions over time.