The Gemelli University Hospital (Italian: Fondazione Policlinico Universitario Agostino Gemelli) is a large general hospital in Rome, Italy. With 1575 beds, it is the second-largest hospital in Italy, the largest hospital in Rome and one of the largest private hospitals in Europe. It serves as the teaching hospital for the medical school of the Università Cattolica del Sacro Cuore (the largest privately owned university in Italy, founded in 1921 in Milan), and owes its name to the university founder, the Franciscan friar, physician and psychologist Agostino Gemelli. The hospital provides free medical assistance as part of the Italian national health system as well as paid-for private assistance in dedicated hotel-style wards.
ABSTRACT Background and Aim Severe obesity is associated with physical comorbidity and impaired health‐related quality of life (HRQoL). Although metabolic and bariatric surgery (MBS) is generally associated with improvements in HRQoL and mental health, inter‐individual variability is substantial, and the functional mechanisms through which preoperative psychological distress may influence postoperative HRQoL trajectories are incompletely characterized. To provide a clearer mechanistic framework, this study examined whether baseline depressive and anxiety symptoms predict 12‐month HRQoL and whether reductions in functional psychosocial impairment mediate these improvements. Methods Single‐center longitudinal cohort of adults with severe obesity undergoing metabolic and bariatric surgery. Assessments at baseline (T0) and 12 months (T1) included the Patient Health Questionnaire–9 (PHQ‐9), Generalized Anxiety Disorder–7 (GAD‐7), Clinical Impairment Assessment (CIA), and the 36‐Item Short Form Health Survey (SF‐36). Bayesian multilevel mediation models (BMLM) adjusted for sociodemographic and clinical covariates were used. Results Participants (n = 251; 76.9% women) showed significant reductions in depressive and anxiety symptoms, psychosocial impairment, and BMI from T0 to T1, alongside improvements in all SF‐36 domains (p < 0.001). Mediation analyses revealed that reductions in psychosocial impairment significantly mediated the relationship between baseline depressive symptoms and HRQoL improvements, explaining up to 42% of the total effect. Anxiety symptoms showed weaker and less consistent mediation. Sensitivity analyses confirmed the robustness of findings across specifications. Conclusion MBS enhances HRQoL and reduces psychological distress. Psychosocial impairment mediates these benefits, especially for depressive symptoms. Integrating psychosocial assessment into pre‐ and postoperative care may help identify vulnerable patients and optimize long‐term outcomes.
Summary Background Valid stratification factors for patients with epithelial ovarian cancer (EOC) are still lacking and individualisation of care remains an unmet need. Radiomics from routine Contrast Enhanced Computed Tomography (CE-CT) is an emerging, highly promising approach towards more accurate prognostic models for the better preoperative stratification of the subset of patients with high-grade-serous histology (HGSOC). However, requirements of fine manual segmentation limit its use. To enable its broader implementation, we developed an end-to-end model that automates segmentation processes and prognostic evaluation algorithms in HGSOC. Methods We retrospectively collected and segmented 607 CE-CT scans across Europe and United States. The development cohort comprised of patients from Hammersmith Hospital (HH) (n=211), which was split with a ratio of 7:3 for training and validation. Data from The Cancer Imagine Archive (TCIA) (United States, n=73) and Kliniken Essen-Mitte (KEM) (Germany, n=323) were used as test sets. We developed an automated segmentation model for primary ovarian cancer lesions in CE-CT scans with U-Net based architectures. Radiomics data were computed from the CE-CT scans. For overall survival (OS) prediction, combinations of 13 feature reduction methods and 12 machine learning algorithms were developed on the radiomics data and compared with convolutional neural network models trained on CE-CT scans. In addition, we compared our model with a published radiomics model for HGSOC prognosis, the radiomics prognostic vector. In the HH and TCIA cohorts, additional histological diagnosis, transcriptomics, proteomics, and copy number alterations were collected; and correlations with the best performing OS model were identified. Predicated probabilities of the best performing OS model were dichotomised using k-means clustering to define high and low risk groups. Findings Using the combination of segmentation and radiomics as an end-to-end framework, the prognostic model improved risk stratification of HGSOC over CA-125, residual disease, FIGO staging and the previously reported radiomics prognostic vector. Calculated from predicted and manual segmentations, our automated segmentation model achieves dice scores of 0.90, 0.88, 0.80 for the HH validation, TCIA test and KEM test sets, respectively. The top performing radiomics model of OS achieved a Concordance index (C-index) of 0.66 ± 0.06 (HH validation) 0.72 ± 0.05 (TCIA), and 0.60 ± 0.01 (KEM). In a multivariable model of this radiomics model with age, residual disease, and stage, the C-index values were 0.71 ± 0.06, 0.73 ± 0.06, 0.73 ± 0.03 for the HH validation, TCIA and KEM datasets, respectively. High risk groups were associated with poor prognosis (OS) the Hazard Ratios (CI) were 4.81 (1.61-14.35), 6.34 (2.08-19.34), and 1.71 (1.10 - 2.65) after adjusting for stage, age, performance status and residual disease. We show that these risk groups are associated with and invasive phenotype involving soluble N -ethylmaleimide sensitive fusion protein attachment receptor (SNARE) interactions in vesicular transport and activation of Mitogen-Activated Protein Kinase (MAPK) pathways. Funding This article represents independent research funded by 1) the Medical Research Council (#2290879), 2) Imperial STRATiGRAD PhD program, 3) CRUK Clinical PhD Grant C309/A31316, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College, London 5) and the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London. Research In Context Evidence before this study Epithelial ovarian cancer (EOC) is the deadliest of all gynaecological cancers, causing 4% of all cancer deaths in women. The most prevalent subtype (70% of EOC patients), high-grade serous ovarian cancer (HGSOC), has the highest mortality rate of all histology subtypes. Radiomics is a non-invasive strategy that has been used to guide cancer management, including diagnosis, prognosis prediction, tumour staging, and treatment response evaluation. To the best of our knowledge, Lu and colleague’s radiomics prognostic vector was the first radiomics model developed and validated to predict overall survival (OS) in HGSOC individuals, from contrast enhanced computed tomography (CE-CT) scans. Both this study and subsequent studies utilised manual segmentations, which adds to the radiologist’s/clinician’s workload and limits widespread use. Additionally, while the models by Lu and co-workers were validated in additional datasets, they were neither harmonised through image resampling – a present requirement for radiomics analysis outlined by the image biomarker standardization initiative – nor compared across machine learning/deep learning models, which could potentially improve predictive performance. Added value of this study The use of adnexal lesion manually delineated segmentations alone to predict outcome is considered demanding and impractical for routine use. By developing a primary ovarian lesion segmentation, our radiomics-based prognostic model could be integrated into the routine ovarian cancer diagnostic workflow, offering risk-stratification and personalised surveillance at the time of treatment planning. Our study is the first to develop an end-to-end pipeline for primary pre-treatment HGSOC prognosis prediction. Several deep learning and machine learning models were compared for prognosis from CE-CT scan-derived, radiomics and clinical data to improve model performance. Implications of all the available evidence Our research demonstrates the first end-to-end HGSOC OS prediction pipeline from CE-CT scans, on two external test datasets. As part of this, we display the first primary ovarian cancer segmentation model, as well as the largest comparative radiomics study using machine learning and deep learning approaches for OS predictions in HGSOC. Our study shows that physicians and other clinical practitioners with little experience in image segmentation can obtain quantitative imaging features from CE-CT for risk stratification. Furthermore, using our prognosis model to stratify patients by risk has revealed sub-groups with distinct transcriptomics and proteomics biology. This work lays the foundations for future experimental work and prospective clinical trials for quantitative personalised risk-stratification for therapeutic-intent in HGSOC-patients.
BACKGROUND:Despite the paucity of outcome data, axillary lymph node dissection (ALND) is increasingly being omitted in patients with positive sentinel lymph nodes after neoadjuvant chemotherapy, particularly in those with low-volume residual disease. We investigated oncological outcomes in patients with breast cancer and residual micrometastases in the sentinel lymph nodes treated with or without ALND. METHODS:OPBC-07/microNAC was a retrospective cohort study, using data obtained from the institutional databases of 84 cancer centres in 30 countries. Patients aged 18 years or older with clinical T1-4, N0-3 breast cancer at diagnosis treated with neoadjuvant chemotherapy followed by surgery between Jan 1, 2013, and May 31, 2023, who were found to have residual micrometastases (metastasis measuring >0·2 mm or >200 cells, not exceeding 2·0 mm in size) on frozen section or on final paraffin sections as determined by sentinel lymph node biopsy, targeted axillary dissection (sentinel lymph node biopsy with single or dual-tracer mapping plus image-guided localisation of the initially biopsy-proven and clipped node), or the marking axillary lymph nodes with radioactive iodine seeds (MARI) procedure were eligible for inclusion. The primary endpoint was the 5-year rate of any axillary recurrence (isolated or combined with local or distant recurrence) stratified by type of axillary surgery. Given the median follow-up, here we report 3-year rates and exploratory 5-year estimates. This study was registered with ClinicalTrials.gov, NCT06529302. FINDINGS:1585 female patients with ypN1mi disease were analysed, of whom 804 (50·7%) underwent ALND and 781 (49·3%) did not. Of 1585 women, 238 (15·0%) self-identified as Asian, 65 (4·1%) as Black, 200 (12·6%) as Hispanic, 968 (61·1%) as White, and 114 (7·2%) as unknown race and ethnicity. 925 (58·4%) of 1585 women had cT2 tumours, 1054 (66·5%) were node positive, and 1267 (79·9%) received nodal radiotherapy. The median follow-up was 3·1 years (IQR 1·8-5·2). The 3-year rate of any axillary recurrence (isolated or combined with local or distant recurrence) for the entire cohort was 2·0% (95% CI 1·3-2·9), with no statistical difference identified by extent of axillary surgery. However, patients with triple-negative disease who did not receive ALND had significantly higher rates of any axillary recurrence than women treated with ALND (8·7% [95% CI 4·4-15·0] vs 2·4% [95% CI 0·7-6·5], p=0·018). On multivariable analysis, triple-negative breast cancer (hazard ratio 3·83 [95% CI 1·72-8·52]) and omission of nodal radiotherapy (2·62 [1·19-5·73]) but not omission of ALND (0·86 [0·37-2·00]) were independently associated with an increased risk of any axillary recurrence. INTERPRETATION:Overall, these results do not support ALND for all patients with ypN1mi on sentinel lymph node biopsy treated with nodal radiotherapy; however, tumour biology should be taken into account when considering ALND omission. FUNDING:US National Institutes of Health, National Cancer Institute.
Background Current clinical decision tools for assessing the risk of symptomatic intracranial hemorrhage (sICH) in patients with vertebrobasilar artery occlusion (VBAO) who received endovascular treatment (EVT) have limited performance. This study develops and validates a clinical risk score to precisely estimate the risk of sICH in VBAO patients.Methods The derivation cohort recruited patients with VBAO who received EVT from the Posterior Circulation IschemIc Stroke Registry in China. Based on the posterior circulation-Alberta Stroke Program Early CT Score (pc-ASPECTS) evaluation method, the cohort was further divided into non-contrast CT (NCCT) and diffusion weighted imaging (DWI) cohorts to construct predictive models. sICH was diagnosed according to the Heidelberg Bleeding Classification within 48 hours of EVT. Clinical signature was constructed in the derivation cohort using machine learning and was validated in two additional cohorts from Asia and Europe.Results We enrolled 1843 patients who underwent EVT and had complete data. pc-ASPECTS of 1710 patients was evaluated on NCCT and 699 patients on DWI. In the NCCT cohort, 1364 individuals made up the training set, of whom 101 (7.4%) developed sICH. In the DWI cohort, the training set consisted of 560 individuals, with 44 (7.9%) experiencing sICH. Predictors of sICH were: glucose, pc-ASPECTS, time from estimated occlusion to groin puncture (EOT), poor collateral circulation, and modified Thrombolysis in Cerebral Infarction (mTICI) score. From these predictors, we derived the weighted poor collateral circulation-EOT-pc-ASPECTS-mTICI-glucose (PEACE) score. The PEACE score showed good discrimination in the training set (area under the curve (AUC)NCCT=0.85; AUCDWI=0.86), internal validation set (AUCNCCT=0.81; AUCDWI=0.82), and two additional external validation set (Asia: AUCNCCT=0.78, AUCDWI=0.80; Europe: AUCNCCT=0.74, AUCDWI=0.78).Conclusion The PEACE score reliably predicted the risk of sICH in VBAO patients who underwent EVT.
The surgical management of pathologic T3a (pT3a) renal cell carcinoma (RCC) remains contentious due to the lack of high-level evidence guiding the choice between partial nephrectomy (PN) and radical nephrectomy (RN). This systematic review and meta-analysis aims to evaluate whether PN represents a safe and effective alternative to RN. A comprehensive search of PubMed, Web of Science, and Scopus was conducted through July 2025. The study included comparative trials of adult pT3a RCC patients undergoing PN or RN, focusing on oncological, perioperative, and functional outcomes. Sixteen retrospective studies involving 34,304 patients (5878 PN; 28426 RN) were analyzed. There were no statistically significant differences between PN and RN regarding estimated blood loss, operative time, hospital stay, or major postoperative complications (Clavien-Dindo > 2). PN was associated with significantly better preservation of renal function (9.96; I2 0
