Valentina Brancato

Herein, we reported the use of Intraoperative‐Contrast Enhanced Ultrasonography (Io‐CEUS) during Robotic Assisted Thoracic Surgery (RATS) for identification of malignant lung nodule that was missed during previous operation. Pre‐operative CT scan with 3D reconstruction defined the prompt position of the target lesion within S8 segment of right lower lobe and the anatomic landmarks as the distance from interlobar fissure and diaphragm. The lesion was then successfully detected by Io‐CEUS. The different arterial enhancement observed during wash‐in (hyperenhancement) and wash‐out (hypo‐enhancement) phases helped to differentiate the nodule from lung parenchyma. The lesion was resected by mechanical stapler and frozen section analysis diagnosed metastasis from colon carcinoma. Post‐operative course was unremarkable; patient was discharged on post‐operative day 3.

Background: Digital pathology generates vast amounts of whole-slide images(WSIs), offering unprecedented opportunities for AI-driven precision medicine.However, the development of robust and generalizable models faces systemic challenges, primarily the scarcity and high cost of high-quality manual annotationsfrom expert clinicians. Efficient selection of representative subsets of datacoresets can reduce annotation effort and computational cost while maintainingmodel performance. This study introduces AIMS (An Informative MeaningfulSubset), a coreset selection strategy and validates it on a use case of prostatecancer (PCa) grading. Methods: This study uses a dataset of 187 H&E-stained whole-slide images(WSIs) from prostatectomy sections, tiled at 20× magnification with 512×512pixel patches. AIMS employs a convolutional autoencoder to learn a compactlatent representation of these tiles, followed by a geometry- and activation-drivensubsampling strategy. The feature space of mean activation vectors is partitioned, and representative coresets (0.5%, 1%, and 2.5% of the full dataset) areselected via cosine similarity. Performance is evaluated using ConvNeXt (trainedfrom scratch) and ResNet18 (with pre-trained UNI embeddings), comparingAIMS-selected subsets to random sampling across datasets with varying characteristics. Model performance is assessed using F1-macro, F1-micro, F1-weighted scores, AUC, and Cohen’s kappa, with additional evaluation of cross-scannergeneralization. Results: Coreset selection via AIMS consistently outperformed random sampling. ConvNeXt trained on AIMS-selected subsets achieved competitive performances using only a fraction of the original data (F1-macro = [0.724-0.782]and AUC = [0.921-0.945]). Cross-scanner evaluation demonstrated robustnessto hardware variability. Similarly, performance were obtained using AIMSselected tiles in the UNI feature space (F1-macro = [0.735-0.742] and AUC =[0.941-0.942]). Conclusion: AIMS-based targeted coreset selection can achieve promising classification performance, representing a viable strategy for substantially reducingannotation costs. This data-efficient approach offers a practical and scalablestrategy for deploying AI in digital pathology, particularly in data-constrainedenvironments, and represents a step toward real-world clinical applicability ofAI-driven PCa diagnosis and grading.

Recent advances in histology scanning technology and Artificial Intelligence (AI) offer great opportunities to support cancer diagnosis. The inability to interpret the extracted features and model predictions is one of the major issues limiting the acceptance of AI models in clinical practice, and a clear representation of the relevance of the extracted features and model predictions is lacking. Focusing on the problem of prostate cancer (PCa) diagnosis and grading, this study aims to detect which are the most discriminant features for distinguishing malignant from non-malignant tissue and Gleason patterns, leaving the evaluation of models’ classification performances as a secondary goal. Utilizing a dataset of 187 annotated H&E-stained whole-slide images, the study explores three magnification levels, extracting 1971 features per tile. Two machine-learning classification tasks are conducted (Malignant vs. Non-malignant and High-grade vs. Low-grade) for each magnification. SHapley Additive exPlanations method was used to investigate models’ interpretability, estimating the importance of pathomic features and their impact on prediction models. Wavelet features were consistently prominent in “High-grade vs Low-grade” classification task, together with local binary pattern descriptors. Some histogram features appeared as key features for diagnostic classification tasks. The identified key discriminant features were classified for their specificity with respect to WSI magnification. Very high AUC values were reached for PCa diagnosis task (0.97 < AUC < 0.99), while task involving Low- versus High-grade classification exhibit lower AUC values (maximum AUC: 0.72–0.73). This work provides new insights for the explanation of hand-crafted pathomic-based classification models for PCa diagnosis and grading. Not applicable.

Uterine corpus endometrial carcinoma (EC) is one of the most common malignancies in the female reproductive system, characterized by tumor heterogeneity at both radiological and pathological scales. Both radiomics and pathomics have the potential to assess this heterogeneity and support EC diagnosis. This study examines the correlation between radiomics features from Apparent Diffusion Coefficient (ADC) maps and post-contrast T1 (T1C) images with pathomic features from pathology images in 32 patients from the CPTAC-UCEC database. 91 radiomics features were extracted from ADC maps and T1C images, and 566 pathomic features from cell detections and cell density maps at four different resolutions. Spearman’s correlation and Bayes Factor analysis were used to evaluate radio-pathomic correlations. Significant cross-scale correlations were found, with strengths ranging from 0.57 to 0.89 in absolute value (9.47 × 10⁴ < BF < 4.77 × 10¹⁴) for the ADC task, and from 0.64 and 0.70 (1.80 × 10⁴ < BF < 5.69 × 10⁵) for the T1C task. Most significant and high cross-scale associations were observed between ADC textural features and features from cell density maps. Correlations involving morphometric features and ADC and T1C first-order features were also observed, reflecting variations in tumor aggressiveness and tissue composition. These findings suggest that correlating radiomic features from ADC and T1C features with histopathological features can enhance understanding of EC intratumoral heterogeneity.

Introduction: Studies on computed tomography (CT) reproducibility at different acquisition parameters have to take into account radiation dose administered and related ethical issues. 3D-printed phantoms provide the possibility to investigate these features deeply and to foster CT research, also taking advantage by outperforming new generation scanners. The aim of this study is to propose a new anthropomorphic 3D-printed phantom for chest lesions, tailored on a real patient CT scan, to investigate the variability of volume and Hounsfield Unit (HU) measurements at different CT acquisition parameters.Methods: The chest CT of a 75-year-old patient with a paramediastinal lung lesion was segmented based on an eight-compartment approach related to HU ranges (air lung, lung interstitium, fat, muscle, vascular, skin, bone, and lesion). From each mask produced, the 3D.stl model was exported and linked to a different printing infill value, based on a preliminary test and HU ratios derived from the patient scan. Fused deposition modeling (FDM) technology printing was chosen with filament materials in polylactic acid (PLA). Phantom was acquired at 50 mAs and three different tube voltages of 80, 100, and 120 kVp on two different scanners, namely, Siemens Somatom Force (Siemens Healthineers, Erlangen, Germany; same setting of real patient for 80 kVp acquisition) and GE 750 HD CT (GE Healthcare, Chicago, IL). The same segmentation workflow was then applied on each phantom acquisition after coregistration pipeline, and Dice Similarity Coefficient (DSC) and HU averages were extracted and compared for each compartment.Results: DSC comparison among real patient versus phantom scans at different kVp, and on both CT scanners, demonstrated a good overlap of different compartments and lesion vascularization with a higher similarity for lung and lesion masks for each setting (about 0.9 and 0.8, respectively). Although mean HU was not comparable with real data, due to the PLA material, the proportion of intensity values for each compartment remains respected.Discussion: The proposed approach demonstrated the reliability of 3D-printed technology for personalized approaches in CT research, opening to the application of the same workflow to other oncological fields.

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

Pooling radiomic features coming from different centers in a statistical framework is challenging due to the variability in scanner models, acquisition protocols, and reconstruction settings. To remove technical variability, commonly called batch effects, different statistical harmonization strategies have been widely used in genomics but less considered in radiomics. The aim of this work was to develop a framework of analysis to facilitate the harmonization of multicenter radiomic features extracted from prostate T2-weighted magnetic resonance imaging (MRI) and to improve the power of radiomics for prostate cancer (PCa) management in order to develop robust non-invasive biomarkers translating into clinical practice. To remove technical variability and correct for batch effects, we investigated four different statistical methods (ComBat, SVA, Arsynseq, and mixed effect). The proposed approaches were evaluated using a dataset of 210 prostate cancer (PCa) patients from two centers. The impacts of the different statistical approaches were evaluated by principal component analysis and classification methods (LogitBoost, random forest, K-nearest neighbors, and decision tree). The ComBat method outperformed all other methods by achieving 70% accuracy and 78% AUC with the random forest method to automatically classify patients affected by PCa. The proposed statistical framework enabled us to define and develop a standardized pipeline of analysis to harmonize multicenter T2W radiomic features, yielding great promise to support PCa clinical practice.

Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.