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Unresectable stage III NSCLC is now treated with chemoradiation (CRT) followed by immune checkpoint inhibitors (ICI). Pneumonitis, a common CRT complication, has heightened risk with ICI, potentially causing severe outcomes. Currently, there are no biomarkers to predict pneumonitis risk or differentiate between radiation-induced pneumonitis (RTP) and ICI-induced pneumonitis (IIP). This study analyzed 293 patients from two institutions, with 140 experiencing pneumonitis (RTP: 84, IIP: 56). Two models were developed: M1 predicted pneumonitis risk using seven radiomic features, achieving high accuracy across internal and external datasets (AUCs: 0.76 and 0.85). M2 differentiated RTP from IIP, with strong performance (AUCs: 0.86 and 0.81). Gene set enrichment analysis linked high pneumonitis risk to pathways such as ECM-receptor interaction and T-cell signaling, while high IIP risk correlated with MAPK and JAK–STAT pathways. Radiomic models show promise in early pneumonitis risk stratification and distinguishing pneumonitis types, potentially guiding personalized NSCLC treatment.

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney’s active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3 , KLF6 , and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1 . Further, combined perturbation of ELF3 , KLF6 , and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks. Comprehensive integration of gene expression with epigenetic features is needed to understand the transition of kidney cells from health to injury. Here, the authors integrate dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and histone modifications to decipher the chromatin landscape of the kidney in reference and adaptive injury cell states, identifying a transcription factor network of ELF3, KLF6, and KLF10 which regulates adaptive repair and maladaptive failed repair.

Background Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods We have developed Histo-Cloud , a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain. Plain language summary Artificial intelligence (AI) is the ability of a computer to conduct complex tasks that humans are capable of performing. AI is useful in the field of pathology, which involves analyzing images of the microscopic structure of different tissues. However, AI can be difficult to set up and apply to the task. One specific task, segmentation, involves picking specific structures out of tissue images and is a prime candidate for automation with AI. In our study, we have created a tool for pathology image segmentation which runs in the cloud (is accessible over the web). We demonstrate the tool by using it to segment various structures from kidney tissue. Our experiments show that the tool is easy to use, accurate, and can estimate the presence of one type of scarring as reliably as human experts. Lutnick et al. develop a cloud-based deep learning tool for whole slide image segmentation. The authors provide several examples of its application in renal pathology, for segmenting glomeruli, interstitial fibrosis and other features of interest.

Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries. We also discuss some of the challenges and potential solutions in attaining equitable AI, including addressing the historically limited representation of diverse populations in existing clinical datasets and the use of inadequate clinical validation methods. Additionally, we focus on extant sources of inequity including the type of model approach (such as deep learning, and feature engineering-based methods), the implications of dataset curation strategies, the need for rigorous validation across a variety of populations and settings, and the risk of introducing contextual bias that comes with developing tools predominantly in high-income countries.Artificial intelligence (AI) has the potential to dramatically change several aspects of oncology including diagnosis, early detection and treatment-related decision making. However, many of the underlying algorithms have been or are being trained on datasets that do not necessarily reflect the diversity of the target population. For this, and other reasons, many AI tools might not be suitable for application in less economically developed countries and/or in patients of certain ethnicities. In this Perspective, the authors discuss possible sources of inequity in AI development, and how to ensure the development and implementation of equitable AI tools for use in patients with cancer.

BackgroundThe landmark study of durvalumab as consolidation therapy in NSCLC patients (PACIFIC trial) demonstrated significantly longer progression-free survival (PFS) in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC) treated with durvalumab (immunotherapy, IO) therapy after chemoradiotherapy (CRT). In clinical practice in the USA, durvalumab continues to be used in patients across all levels of programmed cell death ligand-1 (PD-L1) expression. While immune therapies have shown promise in several cancers, some patients either do not respond to the therapy or have cancer recurrence after an initial response. It is not clear so far who will benefit of this therapy or what the mechanisms behind treatment failure are.MethodsA total of 133 patients with unresectable stage III NSCLC who underwent durvalumab after CRT or CRT alone were included. Patients treated with durvalumab IO after CRT were randomly split into training (D1=59) and test (D2=59) sets and the remaining 15 patients treated with CRT alone were grouped in D3. Radiomic textural patterns from within and around the target nodules were extracted. A radiomic risk score (RRS) was built and was used to predict PFS and overall survival (OS). Patients were divided into high-risk and low-risk groups based on median RRS.ResultsRRS was found to be significantly associated with PFS in D1 (HR=2.67, 95% CI 1.85 to 4.13, p<0.05, C-index=0.78) and D2 (HR=2.56, 95% CI 1.63 to 4, p<0.05, C-index=0.73). Similarly, RRS was associated with OS in D1 (HR=1.89, 95% CI 1.3 to 2.75, p<0.05, C-index=0.67) and D2 (HR=2.14, 95% CI 1.28 to 3.6, p<0.05, C-index=0.69), respectively. RRS was found to be significantly associated with PFS in high PD-L1 (HR=3.01, 95% CI 1.41 to 6.45, p=0.0044) and low PD-L1 (HR=2.74, 95% CI 1.8 to 4.14, p=1.77e-06) groups. Moreover, RRS was not significantly associated with OS in the high PD-L1 group (HR=2.08, 95% CI 0.98 to 4.4, p=0.054) but was significantly associated with OS in the low PD-L1 group (HR=1.61, 95% CI 1.14 to 2.28, p=0.0062). In addition, RRS was significantly associated with PFS (HR=2.77, 95% CI 1.17 to 6.52, p=0.019, C-index=0.77) and OS (HR=2.62, 95% CI 1.25 to 5.51, p=0.01, C-index=0.77) in D3, respectively.ConclusionsTumor radiomics of pretreatment CT images from patients with stage III unresectable NSCLC were prognostic of PFS and OS to CRT followed by durvalumab IO and CRT alone.

Abstract Fetus papyraceous is the intrauterine fetal demise of one of the twins in early pregnancy which gets retained resulting in compression and resembles parchment paper. We report one such case of twin pregnancy where a 23 years primigravida with 36 weeks amenorrhea was referred to our hospital for delivery. Ultrasonography done during antenatal visits revealed twin pregnancy with one dead fetus. She delivered one healthy baby, placenta, and another pale white membranous tissue with firm areas. Multiple sections from the tissue revealed areas of developing bone, cartilage, ciliated epithelium, and muscular tissue confirming the diagnosis of fetus papyraceus. The basic concern of fetus papyraceus is its effect on the surviving fetus and on the mother. To avoid possible complications, intrauterine diagnosis of fetus papyraceus by serial ultrasound examinations and routine placental examination postdelivery to search for fetus papyraceus should be keenly followed.

Introduction: The category of borderline malignancy or unknown malignant potential was added to the WHO's 2017 classification of thyroid tumours. A new histological variety of papillary tumours and Hurthle cell tumours was given as a separate entity. The classification has also adopted the Turin criteria for histological diagnosis of poorly differentiated cancer (PDC). Settings and Design: Descriptive study Methods and Material: From July 2018 to June 2022, 200 thyroid neoplasm patients at a tertiary care facility in western Maharashtra were participated in the prospective research over a period of 4 years. Statistical Analysis Used: The descriptive statistics were used to analyse the collected data. Aim: This study was undertaken to compare the old (2004) and new (2016) WHO classifications and their importance in the treatment of thyroid malignancies. Results: Out of 200 cases, the age range of 31 to 40 years had the greatest number of cases. The ratio of females to males was 5:1. In our study, according to the WHO 2004 classification, malignant tumours comprised 57.5% of the cases, while benign tumours 42.5% of the cases. When tumours were subcategorized, the most frequent benign tumour was follicular adenoma (43.5%) and malignant tumour was papillary thyroid carcinoma (37%). Malignant tumours made up 47.5% of the cases when the tumours were reclassified using the revised WHO 2017 classification, followed by borderline tumours with 27.5% of the cases and benign tumours with 25% of the cases. The most frequent borderline tumour was NIFTP (Noninvasive follicular thyroid neoplasm with papillary-like nuclear features) (17.5%), the most prevalent malignant tumour was papillary carcinoma (including its variant) (32%), and the most frequent benign tumour was follicular adenoma (27%). Conclusion: We concluded that the inclusion of the Boderline Category in the new WHO classification significantly improved thyroid cancer management. WHO 2017 classification prevents under diagnosis (in the case of benign tumors) and over diagnosis (in the case of malignant tumors).

Mixed epithelial and stromal tumor (MEST) is a benign, complex, and rarely encountered renal neoplasm. This case involves a 46-year-old perimenopausal woman who presented with symptoms, such as abdominal pain, burning sensation during urination, increased urinary frequency, and hesitancy. Computed tomography (CT) urography revealed an exophytic, heterogeneously hyperdense mass originating from the interpolar and lower pole parenchyma of the left kidney, suggesting a neoplastic origin. Due to concerns about malignancy and the presence of local symptoms, a laparoscopic-assisted left radical nephrectomy was performed. Histopathological examination of the excised tissue revealed a biphasic neoplasm consisting of epithelial and stromal elements. The epithelial component exhibited cysts and glands of variable sizes, lined by columnar cells and surrounded by stromal tissue. The diagnosis of MESTs of the kidney was established and confirmed through immunohistochemistry. This unique type of benign kidney tumor can be effectively managed through conservative surgery and is associated with a favorable prognosis.

Emerging strains of influenza represent a significant public health threat with potential pandemic consequences. Of particular concern are the recently emerged H7N9 strains which cause pneumonia with acute respiratory distress syndrome. Estimates are that nearly 80% of hospitalized patients with H7N9 have received intensive care unit support. VIS410, a human antibody, targets a unique conserved epitope on influenza A. We evaluated the efficacy of VIS410 for neutralization of group 2 influenza strains, including H3N2 and H7N9 strains in vitro and in vivo. VIS410, administered at 50 mg/kg, protected DBA mice infected with A/Anhui/2013 (H7N9), resulting in significant survival benefit upon single-dose (−24 h) or double-dose (−12 h, +48 h) administration (P< 0.001). A single dose of VIS410 at 50 mg/kg (−12 h) combined with oseltamivir at 50 mg/kg (−12 h, twice daily for 7 d) in C57BL/6 mice infected with A/Shanghai 2/2013 (H7N9) resulted in significant decreased lung viral load (P= 0.002) and decreased lung cytokine responses for nine of the 11 cytokines measured. Based on these results, we find that VIS410 may be effective either as monotherapy or combined with antivirals in treating H7N9 disease, as well as disease from other influenza strains.