Colorectal sessile serrated lesions (SSLs) and hyperplastic polyps (HPs) are characterized by sawtooth or stellate epithelial architecture. Distinguishing between SSLs and HPs is crucial as SSLs are precursors of colorectal carcinomas in 30% of cases, whereas HPs are likely precursors to SSLs. The differentiation of SSL from HP is primarily based on architectural features. Indeed, the hallmark of SSL is a substantial distortion of the typical crypt design and silhouette, which shows horizontal expansion along the muscularis mucosae and enlargement of the crypt base, especially in the lower third of the crypt. The ability to analyze digitized histologic images has led to innovative automated tissue analysis, thereby improving reproducibility and objectivity in pathologists' reports. Some recent studies explored colorectal cancer diagnosis and grading through automated quantitative analysis, but none of them focused on SSL detection. This study aimed to develop an automated method for SSL diagnosis by defining specific metrics to characterize their most common visual features. We developed a processing pipeline involving the automatic segmentation of all the tissue structures required for computing quantitative morphologic and architectural features, which allows detection of SSLs. In particular, we designed a novel metric, digital flatness, which numerically characterizes the parallelism of the gland's contour edges with the muscolaris mucosa profile. In a data set of 759 polyp glands, 41 of which were reported as SSLs by expert pathologists, our novel detection method achieved specificity of 92% and sensitivity of 83%, with accuracy of 92%. Our results represent a first approach to a simple, common, but still debated issue among gastrointestinal pathologists, thus providing valid support for the objective and standardized individuation of SSLs.

Cell segmentation and classification in microscopy images remain challenging due to diverse morphologies and limited semantic context in vision-only models. We present CellVLM, a text-guided vision transformer that augments CelIViT with multimodal capabilities by integrating morphological descriptions through a biomedical text encoder and multi-scale cross-attention. Text guidance is injected at several transformer levels to enhance semantic discrimination among visually similar cell types. We evaluate on the PanNuke dataset with 3-fold crossvalidation and observe consistent improvements over CellViT baselines: multi-class panoptic quality improves to 0.517 (SAM$\mathbf{H} \boldsymbol{;} \mathbf{+ 3. 7 \%}$ relative), with small but positive changes in Dice and detection F1. These findings demonstrate that lightweight textual priors can improve cell-type understanding while preserving established architectural benefits. Code is available at https://github.com/yazeedalrubyli/CellVLM.

Based on the well-known role of peritumour characterization in cancer imaging to improve the early diagnosis and timeliness of clinical decisions, this study innovated a state-of-the-art approach for peritumour analysis, mainly relying on extending tumour segmentation by a predefined fixed size. We present a novel, adaptive method to investigate the zone of transition, bestriding tumour and peritumour, thought of as an annular-like shaped area, and detected by analysing gradient variations along tumour edges. For method validation, we applied it on two datasets (hepatocellular carcinoma and locally advanced rectal cancer) imaged by different modalities and exploited the zone of transition regions as well as the peritumour ones derived by adopting the literature approach for building predictive models. To measure the zone of transition’s benefits, we compared the predictivity of models relying on both “standard” and novel peritumour regions. The main comparison metrics were informedness, specificity and sensitivity. As regards hepatocellular carcinoma, having circular and regular shape, all models showed similar performance (informedness = 0.69, sensitivity = 84%, specificity = 85%). As regards locally advanced rectal cancer, with jagged contours, the zone of transition led to the best informedness of 0.68 (sensitivity = 89%, specificity = 79%). The zone of transition advantages include detecting the peritumour adaptively, even when not visually noticeable, and minimizing the risk (higher in the literature approach) of including adjacent diverse structures, which was clearly highlighted during image gradient analysis.

Nogo-A is a major regulator of neural development and regeneration, but its role in tooth innervation remains largely unknown. Neurons from trigeminal ganglia support teeth homeostasis and regeneration, and disorders of their function could have significant pathophysiological consequences. In this study, we show that Nogo-A is expressed in the trigeminal ganglia and in the neurons innervating the teeth, and that its deletion affects both the number and patterning of neurons in teeth. In organotypic cultures, Nogo-A blocking antibodies affect the trigeminal ganglia-derived neuronal outgrowths and allow premature innervation of tooth germs. RNA sequencing analysis revealed that Nogo-A deletion induces alterations linked to functions at synapses and interference with neurotrophin signalling during the differentiation and maturation of trigeminal neurons. Taken together, these results reveal for the first time the importance of Nogo-A as a major regulator of tooth innervation and point to its potential as a clinical therapeutic target. ### Competing Interest Statement The authors have declared no competing interest.

Locally Recurrent Rectal Cancer (LRRC) remains a major clinical concern; it rapidly invades pelvic organs and nerve roots, causing severe symptoms. Curative-intent salvage therapy offers the only potential for cure but it has a higher chance of success when LRRC is diagnosed at an early stage. Imaging diagnosis of LRRC is very challenging due to fibrosis and inflammatory pelvic tissue, which can mislead even the most expert reader. This study exploited a radiomic analysis to enrich, through quantitative features, the characterization of tissue properties, thus favoring an accurate detection of LRRC by Computed Tomography (CT) and 18F-FDG-Positron Emission Tomography/CT (PET/CT). Of 563 eligible patients undergoing radical resection (R0) of primary RC, 57 patients with suspected LRRC were included, 33 of which were histologically confirmed. After manually segmenting suspected LRRC in CT and PET/CT, 144 Radiomic Features (RFs) were generated, and RFs were investigated for univariate significant discriminations (Wilcoxon rank-sum test, p < 0.050) of LRRC from NO LRRC. Five RFs in PET/CT (p < 0.017) and two in CT (p < 0.022) enabled, individually, a clear distinction of the groups, and one RF was shared by PET/CT and CT. As well as confirming the potential role of radiomics to advance LRRC diagnosis, the aforementioned shared RF describes LRRC as tissues having high local inhomogeneity due to the evolving tissue's properties.

PURPOSE:RNA:DNA hybrids are co-transcriptional products with acknowledged cytoplasmic pro-inflammatory role as activators of the cGAS-STING pathway. We recently proved them also as radiation-induced senescence messages for the abscopal effect mediation, demonstrating the need for a functional p53 for their production and release in A549 and H1299 tumour cells. However, little is known about their role under different stress conditions, especially in cancer cells.METHODS:In this work, we open the investigation making use of automated quantitative imaging to characterize the hybrid subcellular distribution in HeLa cells grown under different oxygen pressures or exposed to different ionizing radiation doses. After cell imaging by confocal fluorescent microscopy, we apply automated imaging methods developed on purpose to quantify hybrid foci and nuclear cluster intensity, regional and local density and dimension.RESULTS:We show that alteration of culture oxygenation increases hybrid cytoplasmic presence, especially when caused by an hyperoxic environment, with evident hybrid gathering at the cell membrane. Ionizing radiations always fail to increase hybrids, in accordance with the absence of functional p53 in HeLa cells. However, dose-dependent effects are still evident and suggest a threshold dose of 7.5 Gy for remarkable hybrid reduction.CONCLUSION:Together with our previous results, these data demonstrate for the first time that different types of stress can increase hybrid production in cancer cells and by at least two different pathways, one p53-dependent triggerable by ionizing radiations and one p53-independent triggerable by oxidative stress. Together, our findings provide a starting point for understanding hybrid role in tumour stress response.

The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal–Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68–0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.

The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.

Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist’s tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), p∼10−5), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10−5; AUC = 0.90 (95%CI, 0.73–0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWIb2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWIb2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWIb2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWIb2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.

Background Recent developments in abscopal effect strongly support the use of radiotherapy for the treatment of metastatic disease. However, deeper understanding of the molecular mechanisms underlying the abscopal effect are required to best benefit a larger proportion of patients with metastasis. Several groups including ours, reported the involvement of wild-type (wt) p53 in radiation-induced abscopal effects, however very little is known on the role of wtp53 dependent molecular mechanisms. Methods We investigated through in vivo and in vitro approaches how wtp53 orchestrates radiation-induced abscopal effects. Wtp53 bearing (A549) and p53-null (H1299) NSCLC lines were xenotransplanted in nude mice, and cultured in 2D monolayers and 3D tumor spheroids. Extracellular vesicles (EVs) were isolated from medium cell culture by ultracentrifugation protocol followed by Nanoparticle Tracking Analysis. Gene expression was evaluated by RT-Real Time, digital qRT-PCR, and dot blot technique. Protein levels were determined by immunohistochemistry, confocal anlysis, western blot techniques, and immunoassay. Results We demonstrated that single high-dose irradiation (20 Gy) induces significant tumor growth inhibition in contralateral non-irradiated (NIR) A549 xenograft tumors but not in NIR p53-null H1299 or p53-silenced A549 (A549sh/p53) xenografts. We further demonstrates that irradiation of A549 cells in vitro induces a senescence-associated secretory phenotype (SASP) producing extracellular vesicles (EVs) expressing CD63 and carrying DNA:RNA hybrids and LINE-1 retrotransposon. IR-A549 EVs also hamper the colony-forming capability of recipient NIR A549 cells, induce senescent phenotype, nuclear expression of DNA:RNA hybrids, and M1 macrophage polarization. Conclusions In our models, we demonstrate that high radiation dose in wtp53 tumors induce the onset of SASP and secretion of CD63+ EVs loaded with DNA:RNA hybrids and LINE-1 retrotransposons that convey senescence messages out of the irradiation field triggering abscopal effect in NIR tumors.

Cellular and subcellular spatial colocalization of structures and molecules in biological specimens is an important indicator of their co-compartmentalization and interaction. Presently, colocalization in biomedical images is addressed with visual inspection and quantified by co-occurrence and correlation coefficients. However, such measures alone cannot capture the complexity of the interactions, which does not limit itself to signal intensity. On top of the previously developed density distribution maps (DDMs), here, we present a method for advancing current colocalization analysis by introducing co-density distribution maps (cDDMs), which, uniquely, provide information about molecules absolute and relative position and local abundance. We exemplify the benefits of our method by developing cDDMs-integrated pipelines for the analysis of molecules pairs co-distribution in three different real-case image datasets. First, cDDMs are shown to be indicators of colocalization and degree, able to increase the reliability of correlation coefficients currently used to detect the presence of colocalization. In addition, they provide a simultaneously visual and quantitative support, which opens for new investigation paths and biomedical considerations. Finally, thanks to the coDDMaker software we developed, cDDMs become an enabling tool for the quasi real time monitoring of experiments and a potential improvement for a large number of biomedical studies.