The complex pathobiology of lung cancer, and its spread worldwide, has prompted research studies that combine radiomic and genomic approaches. Indeed, the early identification of genetic alterations and driver mutations affecting the tumor is fundamental for correctly formulating the prognosis and therapeutic response. In this work, we propose a radiogenomic workflow to detect the presence of KRAS and EGFR mutations using radiomic features extracted from computed tomography images of patients affected by lung adenocarcinoma. To this aim, we investigated several feature selection algorithms to identify the most significant and uncorrelated sets of radiomic features and different classification models to reveal the mutational status. Then, we employed the SHAP (SHapley Additive exPlanations) technique to increase the understanding of the contribution given by specific radiomic features to the identification of the investigated mutations. Two cohorts of patients with lung adenocarcinoma were used for the study. The first one, obtained from the Cancer Imaging Archive (TCIA), consisted of 60 cases (25% EGFR, 23% KRAS); the second one, provided by the Azienda Ospedaliero-Universitaria ’Ospedali Riuniti’ of Foggia, was composed of 55 cases (16% EGFR, 28% KRAS). The best-performing models proposed in our study achieved an AUC of 0.69 and 0.82 on the validation set for predicting the mutational status of EGFR and KRAS, respectively. The Multi-layer Perceptron model emerged as the top-performing model for both oncogenes, in some cases outperforming the state of the art. This study showed that radiomic features can be associated with EGFR and KRAS mutational status in patients with lung adenocarcinoma.

Robot-assisted radical prostatectomy (RARP) has become the most prevalent treatment for patients with organ-confined prostate cancer. Despite superior outcomes, suboptimal vesicourethral anastomosis (VUA) may lead to serious complications, including urinary leakage, prolonged catheterization, and extended hospitalization. A precise localization of both the surgical needle and the surrounding vesical and urethral tissues to coadapt is needed for fine-grained assessment of this task. Nonetheless, the identification of anatomical structures from endoscopic videos is difficult due to tissue distortions, changes in brightness, and instrument interferences. In this paper, we propose and compare two Deep Learning (DL) pipelines for the automatic segmentation of the mucosal layers and the suturing needle in real RARP videos by exploiting different architectures and training strategies. To train the models, we introduce a novel, annotated dataset collected from four VUA procedures. Experimental results show that the nnU-Net 2D model achieved the highest class-specific metrics, with a Dice Score of 0.663 for the mucosa class and 0.866 for the needle class, outperforming both transformer-based and baseline convolutional approaches on external validation video sequences. This work paves the way for computer-assisted tools that can objectively evaluate surgical performance during the critical phase of suturing tasks.

Real-time segmentation of the liver parenchyma is a key requirement for computer-assisted guidance in minimally invasive liver surgery. However, existing methods are developed mainly on public cholecystectomy datasets and are therefore poorly representative of institution-specific acquisition conditions and of the appearance changes introduced by active liver resection. This study investigates public-to-private domain shift in real-time liver segmentation and proposes a SegFormer-B0-based boundary-guided framework that leverages anatomical liver contours and resection-induced boundaries to refine parenchyma segmentation. A surgery-specific augmentation strategy complements the framework by simulating intraoperative perturbations and generating synthetic resection-margin supervision when corresponding annotations are unavailable in the public training data. The method was evaluated on public laparoscopic data and private robot-assisted liver surgery data including both normal anatomy and active resection scenes. The proposed framework consistently outperformed the plain SegFormer-B0 baseline under both Public Training and combined Public + Private Training configurations. In the Public Training setting, the largest improvement was observed on NKI-Resection, where Dice increased from 0.71 to 0.87, while maintaining real-time inference at 75.50 FPS. A fixed library of ten manually annotated private resection images, drawn exclusively from the private training procedures, was used as a source of augmentation patches. Ablation results indicated complementary contributions from surgery-specific augmentation and resection-margin boundary modeling. These findings support boundary-guided refinement and targeted surgical augmentation as promising strategies for improving real-time liver segmentation under domain shift.

Introduction: Colorectal cancer originates in most cases from polyps that progressively become malignant over time and colonoscopy offers a highly studied early diagnostic strategy to ensure a timely treatment plan. Automatic image segmentation of polyps, using intelligent supervised approaches, achieved good performance, but the need of their large annotated datasets limits the clinical applicability. Methods: This study presents MAPSeg (Memory-Augmented Polyp Segmentation), a fully self-supervised and annotation-free framework for colorectal polyp segmentation, trained exclusively on images of healthy mucosa within an anomaly detection paradigm. The key novelty of MAPSeg mainly lies in SIMPO (Simulation of Polyps), a synthetic augmentation strategy that generates realistic polyp shapes and textures in a colon-specific context, combined with a memory-augmented encoder that models structural priors of normal tissue. Results: Extensive experiments demonstrate that MAPSeg outperforms the strongest unsupervised methods by approximately 23% in Intersection over Union and 12% in DICE score on the Hyper-Kvasir dataset and consistently maintains this performance margin across multiple out-of-distribution benchmarks, indicating strong generalization capability. Discussion: The results highlight MAPSeg supported by SIMPO as a viable solution for unsupervised colorectal polyp segmentation, significantly reducing the dependency on manual annotations while maintaining high segmentation accuracy.