The incidence of young-onset pancreatic cancer is rapidly increasing worldwide. However, the association between body mass index (BMI), particularly overweight and mild obesity, and the risk of young-onset pancreatic cancer remains poorly defined. This study aimed to investigate the dose-response relationship between BMI and the risk of young-onset pancreatic cancer.

This nationwide cohort study included 6,315,055 adults aged 20-39 years who underwent national health screenings between 2009 and 2012. BMI categories were defined according to World Health Organization Asia-Pacific guidelines. Participants were followed until December 2020. Multivariable-adjusted Cox proportional hazards models estimated pancreatic cancer risk.

During 59,159,572 person-years of follow-up, 1533 incident pancreatic cancer cases were identified. Compared with individuals with normal weight status, individuals with overweight or class I obesity had a significantly higher risk of pancreatic cancer (adjusted hazard ratio [aHR], 1.389; 95 % CI, 1.210-1.595 and aHR, 1.388; 95 % CI, 1.213-1.588, respectively). Individuals with class II obesity had the highest risk (aHR, 1.958; 95 % CI, 1.585-2.421), whereas underweight individuals had no significantly increased risk (aHR, 1.068; 95 % CI, 0.840-1.360).These associations did not differ significantly across subgroups defined by age, sex, smoking status, alcohol intake, physical activity, or diabetes (all P > 0.05 for interaction).

Overweight and class I obesity during early adulthood may serve as previously underrecognized yet modifiable risk factors for young-onset pancreatic cancer. Proactive weight-control interventions among young adults, starting from overweight status, may help reduce the increasing burden of pancreatic cancer in younger populations.

Everolimus or streptozotocin plus 5-fluorouracil (STZ/5-FU) are approved treatments for patients with pancreatic neuroendocrine tumors (panNETs). The SEQTOR trial aimed to assess the optimal treatment sequence.

SEQTOR was an international, open-label, randomized, crossover, phase III trial that recruited adults with unresectable or metastatic, advanced, well-differentiated panNET. Patients received 10 mg/day of everolimus followed upon progression by STZ/5-FU; or the reverse sequence. The primary endpoint was the 35-month progression-free survival (PFS) rate after first- and second-line treatment; however, due to slow accrual and longer survival, it was changed to the 12-month PFS rate following first-line treatment (12-mPFS₁).

Patients were randomized to everolimus (n = 72) or STZ/5-FU (n = 69) first. The 12-mPFS₁ was 71.4% [95% confidence interval (CI) 59.4% to 81.6%] and 61.8% (95% CI 49.2% to 73.3%) (odds ratio 0.65, 95% CI 0.32-1.32) with a median PFS₁ of 19.4 versus 22.7 months for everolimus and STZ/5-FU, respectively. STZ/5-FU achieved a significantly higher overall response rate in first-line (11.6% versus 30.3%, P = 0.012) and second-line (30.6% versus 9.1%, P = 0.072) treatments. No differences were shown in overall survival (median 61.7 versus 50.6 months in everolimus first and STZ/5-FU first, respectively; hazard ratio 1.43, 95% CI 0.86-2.37). Discontinuations of everolimus were more frequent.

STZ/5-FU and everolimus were not statistically different in PFS rates, but STZ/5-FU achieved higher response rates.

The use of immune checkpoint inhibitors (ICIs) in metastatic colorectal cancer (mCRC) remains limited to tumors harboring microsatellite instability (MSI); however, a subset of microsatellite stable (MSS) tumors features an MSI-like phenotype that could predict responses to ICIs combined with anti-angiogenesis agents.

In this single-arm phase II trial, 45 mCRC patients with a positive tumoral MSI-like gene expression signature (GES) progressing to at least one chemotherapy regimen were recruited from seven European sites within the MoTriColor framework. Of these, 24 and 21 were MSI and MSS, respectively, by standard assays. Patients received intravenous atezolizumab (1200 mg) plus bevacizumab (7.5 mg/kg) infusions in 21-day cycles until progression, unacceptable toxicity, or consent withdrawal. The main outcome measure was the objective response rate (ORR, RECIST 1.1).

The median (interquartile range) age of participants was 63 (58-73) years, 51.1% were male, 60.0% had right-sided tumors, and 31.1% had liver metastases. The ORR in the whole (MSI-like) sample was 38.6% [95% confidence interval (CI) 24.4% to 54.5%]. Among patients with MSI and MSS tumors, the ORR was 65.2% (95% CI 42.7% to 83.6%) and 9.5% (95% CI 1.2% to 30.4%), respectively. In the MSS subgroup without liver metastasis, the ORR was 15.4% (95% CI 1.9% to 45.4%). Overall median progression-free survival was 6.4 (95% CI 4.1-21.2) months (23.2 and 4.0 months in patients with MSI and MSS tumors, respectively). Grade ≥3 adverse events related to atezolizumab and bevacizumab occurred in 5 (11.1%) and 10 patients (22.2%), respectively. There were two grade 5 adverse events, of which one (colonic hemorrhage) was related to bevacizumab.

MSI-like GES does not identify a population with higher sensitivity to immune checkpoint plus angiogenesis inhibition. However, responses are promising in patients with MSI tumors and, to a lesser extent, in patients without liver metastasis regardless of MSI status.

Metabolic dysfunction-associated fatty liver disease (MAFLD), a leading cause of hepatocellular carcinoma (HCC), poses a formidable therapeutic challenge because of the metabolic stress-induced aberrant immune microenvironment. However, no effective pharmacological therapies for the liver microenvironment remodeling in MAFLD are now available. Here, we developed a lipid nanoparticle (Def-LNP) that incorporates vitamin E-derived phosphatidylcholine (VEPC). Def-LNP effectively ameliorated the hepatic oxidative microenvironment to achieve sustained localized expression of target mRNA in hepatocytes in preclinical models, outperforming a commercially used LNP formulation. In vivo delivery efficiency, stability, and biosafety of Def-LNP were validated in various mammalian models, including mice, pigs, and nonhuman primates. Using clinical samples, we identified a pronounced correlation between T cell protein tyrosine phosphatase (TCPTP) and MAFLD pathogenesis. The administration of Def-LNP loaded with TCPTP-encoding mRNA (Def-LNP@mRNA^TCPTP) suppressed signal transducer and activator of transcription signaling in the hepatocytes of MAFLD mice, leading to hepatic metabolic reprogramming and immunological reconfiguration, a characteristic that is prominently lacking in conventional mRNA-based protein replacement therapy. In preclinical models, the administration of Def-LNP@mRNA^TCPTP successfully eliminated steatohepatitis, impeded hepatocarcinogenesis, and improved the therapeutic responsiveness of HCC to cancer vaccine and immune checkpoint blockade therapy. Def-LNP@mRNA^TCPTP represents a potential therapeutic strategy for MAFLD and MAFLD-related HCC, potentially offering treatment paradigms for immunotherapy for HCC and metabolic liver diseases.

Preclinical shortwave infrared/near-infrared II (SWIR/NIR-II, 1,000 to 3,000 nm) fluorescence imaging has shown superior contrast, resolution, and penetration depth compared to traditional near-infrared I (NIR-I, 700 to 900 nm) imaging, owing to reduced light scattering and tissue autofluorescence. Here, we carried out clinical translation of NIR-II fluorescence imaging to guide esophagectomy through intraoperative video imaging and rapid analysis of blood perfusion in the gastric conduits (GC) of esophageal cancer patients, following intravenous administration of indocyanine green (ICG). Within <1 min, NIR-II video imaging clearly visualized the spatial and temporal blood flow features, and importantly, intraoperative principal component analysis (PCA) of the video revealed distinct perfusion patterns in GC. This led to rapid, subjective decision-making for targeted resection of poorly perfused tissue and informed reconstruction of the GC to reduce the risk of life-threatening anastomotic leakage. This approach enhances surgical precision and improves outcomes by providing operator-independent intraoperative guidance.

Despite significant advancements, Breast Cancer (BC) remains a leading cause of morbidity and mortality among women worldwide. The management of BC, as well as the patient prognosis, depends on the tumor invasiveness and grade. Definitive classification of both is provided by biopsy and histopathology. However, this process is both invasive and increases delays in the diagnosis. In this study, an algorithm is developed for the automatic detection and sub-typing of BC type as in situ vs. invasive and tumor grading as grade 2 vs. grade 3 from mammographic images and other patient data. Subtraction of temporally sequential digital mammograms and feature-based Machine Learning (ML) were combined to achieve this goal. The methodology involves two main steps: (1) detection of Regions of Interest (ROIs) and (2) classification of the ROIs. The algorithm was developed using a new dataset of 164 images, with precise annotations. Ninety-six image features and 12 epidemiological and personal history features were collected. Eight feature selection algorithms and 10 classifiers were evaluated for identifying the best models. The algorithm achieved 91.2% accuracy and 0.91 AUC for in situ vs. invasive classification, and 92.8% accuracy and 0.92 AUC for tumor grading. These are the first such results reported in the literature. By reducing the reliance on biopsies, the algorithm provides a faster, non-invasive, and accurate tool for the subtyping of BC. When translated to clinical practice, it has the potential to enhance diagnostic efficiency, improve patient outcomes, and lower BC-related mortality rates.

Medical image segmentation is pivotal for diagnosing and analyzing brain tumors, particularly lower-grade gliomas (LGG). Accurate tumor delineation is critical for clinical decision-making and treatment planning, yet this task remains challenging due to the complex structure of brain tissues and the heterogeneity of tumor characteristics. In this paper, we propose Genomic Attention U-Net (GenAU-net), an enhanced segmentation framework that integrates genomic clustering data into the widely used Attention U-Net architecture. By incorporating patient-specific genomic information, GenAU-net achieves a more personalized approach to LGG MRI segmentation, demonstrating a DICE score of 0.827 on a public LGG dataset. Leveraging genomic data not only improves segmentation performance but also opens avenues for an individualized diagnosis and treatment strategy.Clinical relevance-This research underscores the potential of incorporating genomic information for more accurate LGG segmentation in brain MRI. By providing richer context in the segmentation process, GenAU-net could help clinicians better identify tumor boundaries, optimize surgical resection or radiation therapy plans, and ultimately guide tailored patient care, improving outcomes and survival rates.

Characterization of drug-induced changes in the cancerous cells is important in improving the efficacy of chemotherapeutic drugs and for personalized medicine. This study analyzes the morphological changes in the nuclei objects of cells treated with the drugs targeting Aurora Kinase (AURK) gene family. For this, fluorescence images of lung cancer cell line treated with AMG900 are obtained from a publicly available database. The images are pre-processed and segmented to separate the nuclei objects from the background. Nuclear boundaries are detected, and various shape descriptors, including eccentricity, circularity, convexity, bending energy, and area are computed to comprehensively analyze the drug-induced changes in nuclear morphology. The obtained results show that the bending energy demonstrated high consistency and sensitivity in capturing nuclei irregularities compared to other shape-based metrics, with the highest mean value of 6.71. Nuclei object with a maximum value of bending energy 8.69 exhibit significant boundary variations with increased area and a minimum value of 2 with smooth curvatures. The statistical analysis of the bending energy variations across four replicates resulted in mean bending energies of 6.7, 6.8, 6.5, and 6.5 which indicates the replicate matching morphologies with confirmed reproducibility. Thus, bending energy has proved to be an effective and reliable parameter for measuring the nuclear membrane irregularities in lung cancer cell lines due to chemical or genetic perturbations.Clinical relevance- This irregularity measure can be employed for biocompatibility testing in the standardization of biomedical devices.

Glioblastoma is the most aggressive and common type of malignant primary brain tumor. Neurosurgery is one of the main treatments for the removal of glioblastoma tumors. Although complete tumor resection is crucial, excessive removal of brain tissue can cause unwanted impairment. Intraoperative techniques for tumor detection and delineation can help to achieve a more precise resection and improve the clinical workflow and outcomes. This study explores the use of hyperspectral imaging for detecting glioblastoma during surgery. To this end, a database of 24 images from 14 patients is studied by employing an image analysis framework, which entails spectral and spatial dimensionality reduction and classification. Multiple AI-based methods are presented and tested for the detection of healthy tissue and glioblastoma, as well as techniques for reducing HSI dimensionality, thereby facilitating the clinical applicability of HSI. A multi-layer perceptron shows the highest macro F1 score of 86.65%, when 20 hyperspectral wavelengths are automatically selected by using the Ant Colony optimizer. The proposed approach outperforms the state-of-the-art methods, which use datasets including multiple grades and solely grade 4 tumors. The results demonstrate that HSI combined with a proper image analysis framework, aiming at reducing spectral and spatial dimension, has the potential to aid tumor detection during brain surgery.Clinical RelevanceThis paper demonstrates the feasibility of grade 4 brain tumor detection with hyperspectral image analysis using a set of most informative spectral wavelengths, outperforming the state-of-the-art approaches and paving the way for further advancements and applications of non-invasive imaging techniques to improve image-guided glioblastoma surgery.

Prostate cancer detection and segmentation is a vastly challenging task. Factors such as the low MRI contrast, small lesion size, high inter-observer bias of the ground truth, different acquisition protocols, and scanners with different specifications pose significant difficulties in deep learning segmentation of the region of interest. Additionally, magnetic resonance imaging is used as the baseline decision support image modality, which contributes to the parameter variability of the examined input data. Detecting the region of interest with bounding boxes can be used as a less precise but highly efficient alternative for deep learning-based analyses. In this study, several deep learning architecture variations of the YOLOv8 were used to detect prostate glands and potential neoplasms. The gland detection model achieved a mAP of 95.8±1% on the unseen testing sets, while the best prostate lesion detection model yielded a mAP of 41.5±7%. Additionally, an analysis of the impact of bounding boxes compared to pixel-based annotations on imaging features was carried out. The effect of image quality on gland detection with deep learning-based models was assessed.Clinical Relevance- Annotating large datasets, especially in oncology, is a big barrier for many understaffed research and medical groups. Additionally, the high inter-observer variability can be a significant factor that affects the quality of machine learning models. The proposed cascaded deep learning analysis for detecting prostate lesions, which is a common malignancy in men, can alleviate some of these concerns and can accelerate the tedious and time-consuming lesion annotation process for large multi-modal MRI datasets.