Search Results (1,006)

Background: Gastrointestinal neuroectodermal tumour (GNET), also known as clear cell sarcoma (CCS) of the gastrointestinal tract, is a rare neural crest-derived malignancy characterized by EWSR1-ATF1 or EWSR1-CREB1 fusions. Due to its rarity, there is limited evidence and no established guidelines for standard management. GNET is aggressive, with high rates of local recurrence, metastasis, and mortality. Case Presentation: We report the case of a 46-year-old woman with a family history of gastrointestinal cancers who was diagnosed in 2020 with an intestinal GNET. She underwent a segmental enterectomy as the first step of multimodal therapy. After three years of follow-up, she developed hepatic and peritoneal metastases. In November 2023, she began combined therapy with the anti-VEGF tyrosine kinase inhibitor cabozantinib and the immune checkpoint inhibitor nivolumab. The patient has maintained stable disease for 18 months with good tolerance and no adverse events. Molecular analysis of the tumour, which showed an EWSR1-CREB1 fusion, supported the selection of targeted therapy and immunotherapy as the preferred treatment approach. Conclusions: Immunotherapy and targeted therapy show promise for GNET/CCS treatment, but clinical standards are lacking, and evidence comes primarily from case reports. Additional data are needed to determine the best sequence and combination of therapies for this very rare disease. Full article

Digital twin is a mathematical model that virtually represents a physical object or process and predicts its behavior at future time points. These simulation models enable a deeper understanding of tumorigenic processes and improve biomarker discovery in cancer research. Tumor microenvironment is marked by dysregulated signaling pathways, where kinases and phosphatases serve as critical regulators and promising sources for biomarker discovery. These enzymes operate within multiscale and context-dependent processes where spatial and temporal coordination determine cellular outcomes. Digital Twin technology provides a platform for multimodal and multiscale modeling of kinase and phosphatase processes at the patient-specific level. These models have the potential to transform biomarker validation processes, enhance the prediction of therapeutic responses, and support precision decision-making. In this review, we present the major alterations affecting kinases and phosphatase functions within the tumor microenvironment and their clinical relevance as biomarkers, and we address how digital twins in oncology can augment and refine each stage of the biomarker discovery pipeline. Introducing this emerging technology for cancer biomarker discovery will assist in accelerating its adoption and translation into precision diagnostics and targeted therapies. Full article

Asymmetric nanomotors are a class of self-propelled nanoparticles that exhibit asymmetries in shape, composition, or surface properties. Their unique asymmetry, combined with nanoscale dimensions, endows them with significant potential in environmental and biomedical fields. For instance, glutathione (GSH) induced chemotactic nanomotors can respond to the overexpressed glutathione gradient in the tumor microenvironment to achieve autonomous chemotactic movement, thereby enhancing deep tumor penetration and drug delivery for efficient induction of ferroptosis in cancer cells. Moreover, self-assembled spearhead-like silica nanomotors reduce fluidic resistance owing to their streamlined architecture, enabling ultra-efficient catalytic degradation of lipid substrates via high loading of lipase. This review focuses on three core areas of asymmetric nanomotors: scalable fabrication (covering synthetic methods such as template-assisted synthesis, physical vapor deposition, and Pickering emulsion self-assembly), propulsion mechanisms (chemical/photo/biocatalytic, ultrasound propelled, and multimodal driving), and functional applications (environmental remediation, targeted biomedicine, and microelectronic repair). Representative nanomotors were reviewed through the framework of structure–activity relationship. By systematically analyzing the intrinsic correlations between structural asymmetry, energy conversion efficiency, and ultimate functional efficacy, this framework provides critical guidance for understanding and designing high-performance asymmetric nanomotors. Despite notable progress, the prevailing challenges primarily reside in the biocompatibility limitations of metallic catalysts, insufficient navigation stability within dynamic physiological environments, and the inherent trade-off between propulsion efficiency and biocompatibility. Future efforts will address these issues through interdisciplinary synthesis strategies. Full article

Background: Irradiation (IR) targets cancer cells, but also the tumor microenvironment, including the tumor’s blood vessels. In addition to tumor endothelial cell (TEC) apoptosis, IR can lead to TEC activation, potentially increasing immune cell infiltration. However, the changes underlying the IR-induced activation of endothelial cells (ECs) are poorly understood. This study investigated dose- and time-dependent molecular and functional responses of murine and human EC lines to IR in vitro and TECs in vivo in murine tumor models of colorectal carcinoma. Methods: HUVEC, EA.hy926, and Hulec5a, as well as murine bEND.3, 2H11, and SVEC4-10 EC lines, were irradiated with single doses of 2–10 Gy. EC proliferation and survival after IR were assessed by staining all nuclei (Hoechst 33342) and dead cells (propidium iodide) every 24 h for 5 days using the Cytation 1 Cell Imaging Multi-Mode Reader. RNA sequencing analysis of HUVECs irradiated with 2 Gy and 5 Gy at 24 h and 72 h after IR was conducted, focusing on processes related to EC activation. To validate the RNA sequencing results, immunofluorescence staining for proteins related to EC activation, including Stimulator of Interferon Response cGAMP Interactor 1 (STING), Nuclear factor kappa B (NF-κβ), and Vascular cell adhesion molecule 1 (VCAM-1), was performed. To validate the in vitro results, the response of TEC in vivo was analyzed using publicly available RNA sequencing data of TECs isolated from MC38 colon carcinoma irradiated with a single dose of 15 Gy. Finally, murine CT26 colon carcinoma tumors were immunofluorescently stained for STING and NF-κβ 24 and 48 h after IR with a clinically relevant fractionated regimen of 5 × 5 Gy. Results: Doses of 2, 4, 6, 8, and 10 Gy led to a dose-dependent decrease in proliferation and increased death of ECs. RNA sequencing analysis showed that the effects on the transcriptome of HUVECs were most pronounced 72 h after IR with 5 Gy, with 1014 genes (661 down-regulated and 353 up-regulated) being significantly differentially expressed. Irradiation with 5 Gy resulted in HUVEC activation, with up-regulation of the immune system and extracellular matrix genes, such as STING1 (log₂FC = 0.81) and SELE (log₂FC = 1.09), respectively; and down-regulation of cell cycle markers. Furthermore, IR led to the up-regulation of immune response- and extracellular matrix (ECM)-associated signaling pathways, including NF-κβ signaling and ECM–receptor interaction, which was also observed in the transcriptome of irradiated murine TECs in vivo. This was confirmed at the protein level with higher expressions of the EC activation-associated proteins STING, NF-κβ, and VCAM-1 in irradiated HUVECs and irradiated TECs in vivo. Conclusions: IR induces changes in ECs and TECs, supporting their activation in dose- and time-dependent manners, potentially contributing to the anti-tumor immune response, which may potentially increase the infiltration of immune cells into the tumor and thus, improve the overall efficacy of RT, especially in combination with immune checkpoint inhibitors. Full article

Rectal cancer remains a significant clinical challenge due to its complex anatomy and the critical need to balance oncological radicality with functional preservation. Multimodal treatment strategies, including neoadjuvant therapy, advanced endoscopic techniques, and precise surgical approaches, have evolved to optimize patient outcomes. Neoadjuvant chemoradiotherapy improves resectability and local control in locally advanced tumors, while endoscopic treatment offers organ-preserving options for carefully selected early-stage cancers. Surgical resection, primarily through total mesorectal excision (TME), remains the cornerstone of curative therapy, with minimally invasive and transanal approaches enhancing precision and recovery. In advanced and recurrent cases, extended procedures such as pelvic exenteration provide potential for cure despite substantial morbidity. This review summarizes current evidence on the indications, techniques, and outcomes of neoadjuvant, endoscopic, and surgical treatments for rectal cancer, emphasizing individualized treatment planning to achieve optimal oncological and functional results. Full article

Background: Breast cancer remains a critical public health problem worldwide and is a leading cause of cancer-related mortality. Optimizing clinical outcomes is contingent upon the early and precise detection of malignancies. Advances in medical imaging and artificial intelligence (AI), particularly in the fields of radiomics and deep learning (DL), have contributed to improvements in early detection methodologies. Nonetheless, persistent challenges, including limited data availability, model overfitting, and restricted generalization, continue to hinder performance. Methods: This study aims to overcome existing challenges by improving model accuracy and robustness through enhanced data augmentation and the integration of radiomics and deep learning features from the CBIS-DDSM dataset. To mitigate overfitting and improve model generalization, data augmentation techniques were applied. The PyRadiomics library was used to extract radiomics features, while transfer learning models were employed to derive deep learning features from the augmented training dataset. For radiomics feature selection, we compared multiple supervised feature selection methods, including RFE with random forest and logistic regression, ANOVA F-test, LASSO, and mutual information. Embedded methods with XGBoost, LightGBM, and CatBoost for GPUs were also explored. Finally, we integrated radiomics and deep features to build a unified multimodal feature space for improved classification performance. Based on this integrated set of radiomics and deep learning features, 13 pre-trained transfer learning models were trained and evaluated, including various versions of ResNet (50, 50V2, 101, 101V2, 152, 152V2), DenseNet (121, 169, 201), InceptionV3, MobileNet, and VGG (16, 19). Results: Among the evaluated models, ResNet152 achieved the highest classification accuracy of 97%, demonstrating the potential of this approach to enhance diagnostic precision. Other models, including VGG19, ResNet101V2, and ResNet101, achieved 96% accuracy, emphasizing the importance of the selected feature set in achieving robust detection. Conclusions: Future research could build on this work by incorporating Vision Transformer (ViT) architectures and leveraging multimodal data (e.g., clinical data, genomic information, and patient history). This could improve predictive performance and make the model more robust and adaptable to diverse data types. Ultimately, this approach has the potential to transform breast cancer detection, making it more accurate and interpretable. Full article

This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy—though validation in BTC-specific cohorts remains essential—accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7–10 years, contingent on successful completion of validation studies addressing current evidence gaps. Full article

Breast cancer remains a critical global health challenge, with over 2.1 million new cases annually. This review systematically evaluates recent advancements (2022–2024) in machine and deep learning approaches for breast cancer detection and risk management. Our analysis demonstrates that deep learning models achieve 90–99% accuracy across imaging modalities, with convolutional neural networks showing particular promise in mammography (99.96% accuracy) and ultrasound (100% accuracy) applications. Tabular data models using XGBoost achieve comparable performance (99.12% accuracy) for risk prediction. The study confirms that lifestyle modifications (dietary changes, BMI management, and alcohol reduction) significantly mitigate breast cancer risk. Key findings include the following: (1) hybrid models combining imaging and clinical data enhance early detection, (2) thermal imaging achieves high diagnostic accuracy (97–100% in optimized models) while offering a cost-effective, less hazardous screening option, (3) challenges persist in data variability and model interpretability. These results highlight the need for integrated diagnostic systems combining technological innovations with preventive strategies. The review underscores AI’s transformative potential in breast cancer diagnosis while emphasizing the continued importance of risk factor management. Future research should prioritize multi-modal data integration and clinically interpretable models. Full article

Background: Spinal metastasis is the third most common site for metastatic localization, following the lung and liver. Manual detection through imaging modalities such as CT, MRI, PET, and bone scintigraphy can be costly and inefficient. Preliminary artificial intelligence (AI) techniques and computer-aided detection (CAD) systems have attempted to improve lesion detection, segmentation, and treatment response in oncological imaging. The objective of this review is to evaluate the current applications of AI across multimodal imaging techniques in the diagnosis of spinal metastasis. Methods: Databases like PubMed, Scopus, Web of Science Advance, Cochrane, and Embase (Ovid) were searched using specific keywords like ‘spine metastases’, ‘artificial intelligence’, ‘machine learning’, ‘deep learning’, and ‘diagnosis’. The screening of studies adhered to the PRISMA guidelines. Relevant variables were extracted from each of the included articles such as the primary tumor type, cohort size, and prediction model performance metrics: area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, internal validation and external validation. A random-effects meta-analysis model was used to account for variability between the studies. Quality assessment was performed using the PROBAST tool. Results: This review included 39 studies published between 2007 and 2024, encompassing a total of 6267 patients. The three most common primary tumors were lung cancer (56.4%), breast cancer (51.3%), and prostate cancer (41.0%). Four studies reported AUC values for model training, 16 for internal validation, and five for external validation. The weighted average AUCs were 0.971 (training), 0.947 (internal validation), and 0.819 (external validation). The risk of bias was the highest in the analysis domain, with 22 studies (56%) rated high risk, primarily due to inadequate external validation and overfitting. Conclusions: AI-based approaches show promise for enhancing the detection, segmentation, and characterization of spinal metastatic lesions across multiple imaging modalities. Future research should focus on developing more generalizable models through larger and more diverse training datasets, integrating clinical and imaging data, and conducting prospective validation studies to demonstrate meaningful clinical impact. Full article

Background/Objectives: Immunotherapy is a viable therapeutic approach for non-small cell lung cancer (NSCLC). Despite the significant survival benefit of immune checkpoint inhibitors PD-1/PD-L1, on average; the objective response rate is around 20% as monotherapy and around 50% in combination with chemotherapy. While PD-L1 IHC is used as a predictive biomarker, its accuracy is subpar. Methods: In this work, we develop a machine learning (ML) method to predict response to immunotherapy in NSCLC from multimodal clinicopathological biomarkers, tumor and peritumoral radiomic biomarkers from CT images. We further learn a graph structure to understand the associations between biomarkers and treatment response. The graph is then used to create sentences with clinical hypotheses that are finally used in a Large Language Model (LLM) that explains the treatment response predicated on the biomarkers that are comprehensible to clinicians. From a retrospective study, a training dataset of NSCLC with n = 248 tumors from 140 subjects was used for feature selection, ML model training, learning the graph structure, and fine-tuning LLM. Results: An AUC = 0.83 was achieved for prediction of treatment response on a separate test dataset of n = 84 tumors from 47 subjects. Conclusions: Our study therefore not only improves the prediction of immunotherapy response in patients with NSCLC from multimodal data but also assists the clinicians in making clinically interpretable predictions by providing language-based explanations. Full article

Poorly differentiated thyroid malignancy, a rare histological type of aggressive thyroid malignancy with associated difficulties and gaps in its histological and molecular characterization, might lead to challenging clinical presentations that require a prompt multimodal approach. This case study involved a 56-year-old, non-smoking male with a rapidly developing goiter (within 2–3 months) in association with mild, non-specific neck compressive symptoms. His medical history was irrelevant. A voluminous goiter with substernal and posterior extension up to the vertebral bodies was detected using an ultrasound and computed tomography (CT) scan and required emergency thyroidectomy. He had normal thyroid function, as well as negative thyroid autoimmunity and serum calcitonin. The surgery was successful upon “Y” incision, which was used to give better access to the retrosternal component in order to avoid a sternotomy. Post-operatively, the subject developed hypoparathyroidism-related hypocalcemia and showed a very high serum thyroglobulin level (>550 ng/mL). The pathological report confirmed poorly differentiated, multifocal thyroid carcinoma (with an insular, solid, and trabecular pattern) against a background of papillary carcinoma (pT3b, pN0, and pM1; L1; V2; Pn0; R1; and stage IVB). The subject received 200 mCi of radioiodine therapy for 6 weeks following the thoracic surgery. Whole-body scintigraphy was performed before radioiodine therapy and showed increased radiotracer uptake at the thyroid remnants and pre-tracheal levels. Additionally, single-photon emission computed tomography combined with CT (SPECT/CT) was performed, and confirmed the areas of intense uptake, in addition to a moderate uptake in the right and left pulmonary parenchyma, suggesting lung metastasis. To conclude, an overall low level of statistical evidence exists regarding poorly differentiated malignancy in substernal goiters, and the data also remains scarce regarding the impact of genetic and molecular configurations, such as the BRAF-positive profile, in this specific instance. Furthermore, multimodal management includes additional diagnosis methods such as SPECT/CT, while long-term multilayered therapy includes tyrosine kinase inhibitors if the outcome shows an iodine-resistant profile with a poor prognosis. Awareness remains a key factor in cases of a poorly differentiated carcinoma presenting as a rapidly growing goiter with substernal extension in an apparently healthy adult. A surgical approach, while varying with the surgeon’s skills, represents a mandatory step to ensure a better prognosis. In addition to a meticulous histological characterization, genetic/molecular features provide valuable information regarding the outcome and can further help with the decision to use new anti-cancer drugs if tumor response upon radioiodine therapy is no longer achieved; such a development is expected in this disease stage in association with a BRAF-positive configuration. Full article

Background: Circulating tumor DNA (ctDNA), shed into bodily fluids by cancer cells through apoptosis, necrosis, or active secretion, is currently used in the field of genomic investigation in clinical settings, primarily for advanced stages of non-small-cell lung cancer (NSCLC). However, its potential role in guiding the multi-omic approach to early-stage NSCLC is emerging as a promising area of investigation. Efforts are being made to integrate the genomics not only in surgery, but also in the definition of long-term prognosis after surgical or radiotherapy and for the prediction of recurrence. Methods: An extensive literature search was conducted on PubMed, covering publications from 2000 to 2024. Using the advanced search tool, titles and abstracts were filtered based on the following keywords: ctDNA, early stage, NSCLC. From this search, 20 studies that fulfilled all inclusion criteria were selected for analysis in this review. Results: This review highlights the growing body of evidence supporting the potential clinical use of ctDNA as a genomic biomarker in managing early-stage NSCLC. Baseline ctDNA levels offer valuable information about tumor molecular biology and histological characteristics. Beyond its prognostic value before treatment, liquid biopsy has proven useful for tracking minimal residual disease and forecasting recurrence following curative interventions such as surgery or radiotherapy. Future adjuvant treatment decisions may increasingly rely on predictive models that incorporate liquid biopsy findings alongside other clinical factors. Conclusions: The potential use of this analyte introduces new opportunities for the integration of genomic data in treatment, as well as relapse monitoring with more accurate and innovative than traditional methods, particularly in patients with early-stage NSCLC Full article

Background: Skin cancer diagnosis faces critical challenges due to the visual similarity of lesions and dataset limitations. Methods: This study introduces HybridSkinFormer, a robust deep learning model designed to classify skin lesions from both clinical and dermatoscopic images. The model employs a two-stage architecture: a multi-layer ConvNet for local feature extraction and a residual-learnable multi-head attention module for global context fusion. A novel activation function (StarPRelu) and Enhanced Focal Loss (EFLoss) address neuron death and class imbalance, respectively. Results: Evaluated on a hybrid dataset (37,483 images across nine classes), HybridSkinFormer achieved state-of-the-art performance with an overall accuracy of 94.2%, a macro precision of 91.1%, and a macro recall of 91.0%, outperforming nine CNN and ViT baselines. Conclusions: Its ability to handle multi-modality data and mitigate imbalance highlights its clinical utility for early cancer detection in resource-constrained settings. Full article

Background/Objectives: Colorectal cancer (CRC) ranks third in the Western world in cancer incidence and second as the cause of cancer-related deaths. Despite advances in perioperative care, minimizing postoperative morbidity is crucial in clinical practice. Digitalization of the healthcare process plays a key role in genuinely and effectively engaging patients. Our aim was to evaluate a digital solution for remote monitoring of patients with CRC, from surgery indication to postoperative discharge. Methods: We developed a digital solution using Value Stream Mapping (VSM) to identify patient care flow and Lean Sigma for optimization and efficiency. We incorporated the Enhanced Recovery After Surgery (ERAS)/RICA pentamodal recommendations to create a program with an individualized schedule for each patient, who received tailored educational, medical, and practical information at every stage of the process. Results: A total of 193 patients used the digital solution, with >75% adhering to ERAS recommendations. The median length of hospital stay was 5 days, with low adherence leading to 3.4 (p = 0.628) or 3.27 (p = 0.642) extra days in the hospital compared to patients with intermediate and high adherence, respectively. The mean comprehensive complication index (CCI) was 9.1/100, which was higher in patients with low adherence (15) versus intermediate (8.17; p = 0.027) and high (7.42; p = 0.011) adherence. An increase in self-perception of quality of life by 9.2% was identified at the end of the process compared to the outcome at the beginning (p = 0.09), and 80% rated their overall satisfaction with the care process as 8 or higher out of 10. Conclusions: The digital solution facilitates the monitoring of CRC care and implementation and adherence to ERAS recommendations, improving patient engagement and satisfaction. Full article

Background: Muscle-invasive bladder cancer (MIBC) is associated with high recurrence and mortality rates. While cisplatin-based neoadjuvant chemotherapy followed by radical cystectomy remains the standard of care, many patients are ineligible for cisplatin. Recent advances in immunotherapy and biomarker research are reshaping perioperative strategies, aiming to personalize treatment and improve outcomes. Methods: We conducted a comprehensive narrative review of the recent literature and clinical trials on the perioperative treatment of MIBC. We focused on published phase II and III trials assessing neoadjuvant and adjuvant strategies, including immunotherapy, antibody-drug conjugates (ADCs), combination regimens, and circulating tumor DNA (ctDNA)-based approaches. Results: Numerous trials (e.g., PURE-01, ABACUS, NABUCCO, AURA, NIAGARA) have demonstrated the feasibility and efficacy of immune checkpoint inhibitors (ICIs) in both cisplatin-eligible and -ineligible populations. Combination strategies, including ICIs plus chemotherapy or ADCs, have shown promising pathological complete response rates and event-free survival. In the adjuvant setting, nivolumab improved disease-free survival and received regulatory approval. Biomarkers such as PD-L1 and ctDNA are emerging tools for predicting treatment response and recurrence risk, although prospective validation is ongoing. Conclusions: The treatment paradigm for MIBC is shifting toward multimodal and biomarker-driven approaches. Integration of ICIs into perioperative management, especially in combination with chemotherapy or ADCs, may enhance outcomes. ctDNA shows potential as a predictive and prognostic biomarker, guiding therapeutic decisions and surveillance. Future research should focus on refining patient selection, optimizing treatment sequencing, and validating ctDNA-guided strategies to personalize care while minimizing overtreatment. Full article