Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Binbin Han, Binbin Han, Xiaofang Chen

BackgroundLung cancer places a substantial burden on hospital inpatient care, particularly in tertiary cancer centers. Evidence remains limited on how hospital performance-based management reforms are associated with inpatient efficiency and costs among patients with lung cancer.MethodsWe conducted a retrospective, real-world study using inpatient administrative data from a tertiary cancer hospital in China between 2016 and 2020. Hospitalizations (admissions) of patients with lung cancer were identified, and patient records were linked to enable secondary patient-level analyses. Length of stay (LOS) and daily hospitalization costs were evaluated as complementary indicators of inpatient efficiency and resource utilization intensity. A hospital performance reform implemented in April 2018 was examined by comparing pre-reform (2016–2017) and post-reform (2019–2020) periods. An interrupted time series analysis (ITSA) was conducted using segmented regression on monthly geometric means of log-transformed outcomes at the hospitalization level. Multivariable patient-level regression analyses were conducted as secondary analyses.ResultsA total of 25,331 patients hospitalized with lung cancer were included. After April 2018, LOS declined by approximately 1.6% per month (p < 0.001) relative to the pre-reform trend, while daily hospitalization costs increased by approximately 2.1% per month (p < 0.001) relative to the pre-reform trend. Patient-level analyses were directionally consistent, with the post-reform period associated with a 16.0% shorter LOS and a 31.9% higher daily cost. Sensitivity analyses excluding 2020 and restricting to index admissions yielded similar results.ConclusionsAmong patients hospitalized with lung cancer, the hospital performance reform implemented in 2018 was associated with shorter hospitalization duration and higher daily costs. These findings suggest concurrent changes in inpatient efficiency and resource utilization intensity and highlight the importance of using complementary indicators when evaluating hospital management reforms in oncology care.

Andrea Maturana-Ramirez, Andrea Maturana-Ramirez, Juan Aitken-Saavedra et al.

IntroductionOral leukoplakia is one of the most frequent oral potentially malignant disorders. The present study aims to compare serum vitamin D levels between patients with and without oral leukoplakia, by smoking habit.MethodsThis cross-sectional study involved a group of 45 cases with oral leukoplakia and a control group with 45 individuals. In both groups a pathology report was done, and for leukoplakia a binary classification of low- and high-grade epithelial dysplasia was employed. Serum 25(OH)D3 vitamin D levels, and data on smoking status, age, gender, comorbidities, and clinical and pathological characteristics were collected for both groups.Resultsvitamin D levels were lower in the oral leukoplakia group with a median of 19.1 ng/ml, while the control group had a median of 24.8 ng/ml. When subdividing each group by smoking habit, the smoking case group had a median of 19.4 ng/ml (IQR: 15.7-21.5 ng/ml), the non-smoking case group had 18.8 ng/ml (IQR: 13.6-29.2 ng/ml), the smoking control group had 21.8 ng/ml (IQR: 17.5-27.3 ng/ml), and the non-smoking control group had 25.4 ng/ml (IQR: 20.4-32.9 ng/ml) (p<0.05). When comparing serum vitamin D levels, statistically significant differences were found between the smoking case group versus the non-smoking control group and between the non-smoking case group versus the non-smoking control group (p<0.05). Serum vitamin D levels by histopathological diagnosis showed no differences between leukoplakia groups.DiscussionThis study shows that serum vitamin D levels were lower in patients with OL compared to those without OL, which was more evident in the smoking group. Patients with OL were previously observed to have hypovitaminosis D, without assessing smoking habits. This finding suggests a possible role of vitamin D deficiency in the development of OL, which could be more marked in smokers. This opens the possibility of future research on vitamin D as a chemopreventive agent in the malignant transformation of OL, and to evaluate the relationship between smoking and hypovitaminosis D.

Huimin Xue, Xin Sun, Xiaomei Yang et al.

BackgroundRadiation osteitis (RO) is a bone-related complication following radiotherapy (RT), often characterized by atypical imaging features. It is challenging to distinguish RO from early bone metastasis (BM), potentially leading to inappropriate treatment. Therefore, establishing reliable diagnostic criteria for accurately identifying RO is essential for improving treatment outcomes in advanced colorectal cancer (CRC).Case descriptionTwo cases of advanced CRC patients with atypical isolated bone changes on imaging are presented. Both patients received standard chemotherapy and radiotherapy after surgery. Through comprehensive imaging studies, laboratory evaluations, and multidisciplinary team (MDT) consultations, the diagnosis of RO was confirmed instead of BM, thereby avoiding the need for an invasive pathological biopsy.ConclusionsThis case report highlights imaging features of RO and provides valuable insights into differentiating RO from BM by integrating medical history, laboratory findings, and imaging results.

Chetan Deshmukh, R S Shendge, Rutik J Jadhav

Context: Androgenic alopecia (AGA) is a common condition affecting both men and women, characterized by progressive hair loss due to genetic and hormonal factors. Hair loss has significant impacts on psychosocial well-being and quality of life. Evidence Acquisition: A comprehensive review of peer-reviewed studies was conducted, including clinical trials, observational studies, and emerging treatment reports published from 2000 to 2024. Databases such as PubMed, Scopus, and Web of Science were searched using keywords related to AGA, hair growth, and therapies. Results: Current treatments for AGA include topical agents like minoxidil and finasteride, oral medications, and advanced options such as hair transplantation. Emerging therapies, including platelet-rich plasma (PRP), low-level laser therapy (LLLT), JAK inhibitors, and gene therapy, show promising efficacy in promoting hair regrowth. Combination therapies often enhance clinical outcomes. Conclusions: While traditional treatments remain effective, emerging therapies and combination approaches offer improved results for AGA management. Ongoing research in gene therapy and novel molecular interventions may transform future therapeutic strategies.

Yan Shen, Yan Shen, Yaxin Xu et al.

Gastric cancer remains one of the most prevalent gastrointestinal malignancies, with certain subtypes, such as poorly cohesive carcinoma—including signet ring cell carcinoma (SRCC)—exhibiting aggressive progression and poor prognosis. Mesenchymal epithelial transition (MET) amplification, a relatively rare oncogenic driver in gastric cancer (~2–10.2% of cases), has been associated with resistance to conventional therapies and dismal survival (median <6 months in metastatic cases). While MET inhibitors such as crizotinib have shown efficacy in MET-altered non-small cell lung cancer (NSCLC), their role in gastric cancer remains uncertain due to tumor heterogeneity and the lack of robust clinical evidence. We report a case of a female patient with MET-amplified metastatic gastric cancer and systemic bone marrow involvement. Despite eventual disease progression, the initial response to crizotinib was remarkable, with rapid hematologic recovery (platelets: 7→216×109/L) and significant tumor regression. Although disease progression occurred after 5 months, characterized by pulmonary metastasis, biliary obstruction and multiple infections, the substantial initial benefits of crizotinib cannot be overlooked. The patient survived 8 months from diagnosis, highlighting the transient efficacy of MET inhibition and the impact of clonal evolution. This case underscores the potential and limitations of MET inhibitors in gastric cancer. Biomarker-driven selection, early resistance detection, and trials exploring crizotinib-chemotherapy/immunotherapy combinations are urgently needed to improve outcomes in this aggressive subtype.

Liangrui Pan, Qingchun Liang, Shen Zhao et al.

Accurately predicting gene mutations, mutation subtypes and their exons in lung cancer is critical for personalized treatment planning and prognostic assessment. Faced with regional disparities in medical resources and the high cost of genomic assays, using artificial intelligence to infer these mutations and exon variants from routine histopathology images could greatly facilitate precision therapy. Although some prior studies have shown that deep learning can accelerate the prediction of key gene mutations from lung cancer pathology slides, their performance remains suboptimal and has so far been limited mainly to early screening tasks. To address these limitations, we have assembled PathGene, which comprises histopathology images paired with next-generation sequencing reports from 1,576 patients at the Second Xiangya Hospital, Central South University, and 448 TCGA-LUAD patients. This multi-center dataset links whole-slide images to driver gene mutation status, mutation subtypes, exon, and tumor mutational burden (TMB) status, with the goal of leveraging pathology images to predict mutations, subtypes, exon locations, and TMB for early genetic screening and to advance precision oncology. Unlike existing datasets, we provide molecular-level information related to histopathology images in PathGene to facilitate the development of biomarker prediction models. We benchmarked 11 multiple-instance learning methods on PathGene for mutation, subtype, exon, and TMB prediction tasks. These experimental methods provide valuable alternatives for early genetic screening of lung cancer patients and assisting clinicians to quickly develop personalized precision targeted treatment plans for patients. Code and data are available at https://github.com/panliangrui/NIPS2025/.

Natnael Alemayehu

This study presents a convolutional neural network (CNN)-based approach for the multi-class classification of brain tumors using magnetic resonance imaging (MRI) scans. We utilize a publicly available dataset containing MRI images categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor. Our primary objective is to build a light weight deep learning model that can automatically classify brain tumor types with high accuracy. To achieve this goal, we incorporate image preprocessing steps, including normalization, data augmentation, and a cropping technique designed to reduce background noise and emphasize relevant regions. The CNN architecture is optimized through hyperparameter tuning using Keras Tuner, enabling systematic exploration of network parameters. To ensure reliable evaluation, we apply 5-fold cross-validation, where each hyperparameter configuration is evaluated across multiple data splits to mitigate overfitting. Experimental results demonstrate that the proposed model achieves a classification accuracy of 98.78%, indicating its potential as a diagnostic aid in clinical settings. The proposed method offers a low-complexity yet effective solution for assisting in early brain tumor diagnosis.

Juan Jose Juarez-Vignon Whaley, Prateek Pophali, Yevgen Chornenkyy et al.

Introduction: Cancers of unknown primary are aggressive and rare malignancies with a complex diagnosis and management. Here we present a case in which imaging, pathology, and molecular biology did not match for a specific tumor site and the importance of a multidisciplinary team for these complicated cases. Case Presentation: A man in his 70s with strong smoking history under workup for suspicion of metastatic lung cancer underwent lung mass biopsy. Immunohistochemical stains corresponded to hepatocellular/cholangiocarcinoma or germ cell tumor; however, dedicated liver and testicular studies including imaging and iscochrome 12p FISH were negative. Additionally, somatic variant profiling was not specific for any malignancy nor targetable variants. Given the pattern of disease, risk factors, and patient history, the patient received treatment for lung adenocarcinoma (carboplatin, pemetrexed, and pembrolizumab). The patient had a drastic improvement in dyspnea, weight gain, and was able to return to work. Conclusion: This report describes a case in which immunohistochemistry and molecular profiling did not identify the tissue of origin and highlights the importance of a multidisciplinary team to reach a diagnosis and guide treatment without delaying patient care in patients with these diagnoses.

Xu Yan, Weimin Wang, MingXuan Xiao et al.

Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies with high mortality rates and complex treatment landscapes. In response to the critical need for accurate prognosis in cancer patients, the medical community has embraced the 5-year survival rate as a vital metric for estimating patient outcomes. This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients. Leveraging advanced image analysis techniques, we sliced whole slide images (WSI) of these cancers, extracting comprehensive features to capture nuanced tumor characteristics. Subsequently, we constructed patient-level graphs, encapsulating intricate spatial relationships within tumor tissues. These graphs served as inputs for a sophisticated 4-layer graph convolutional neural network (GCN), designed to exploit the inherent connectivity of the data for comprehensive analysis and prediction. By integrating patients' total survival time and survival status, we computed C-index values for gastric cancer and Colon adenocarcinoma, yielding 0.57 and 0.64, respectively. Significantly surpassing previous convolutional neural network models, these results underscore the efficacy of our approach in accurately predicting patient survival outcomes. This research holds profound implications for both the medical and AI communities, offering insights into cancer biology and progression while advancing personalized treatment strategies. Ultimately, our study represents a significant stride in leveraging AI-driven methodologies to revolutionize cancer prognosis and improve patient outcomes on a global scale.

Aydah Al-Awadhi, Humaid O. Al-Shamsi

AbstractNeuroendocrine tumors (NETs), despite their increasing incidence, are considered to be rare tumors. There is no published data about the incidence or prevalence of NETs in the UAE. In a survey in 2021 for oncologists in the UAE, 43 respondents completed the survey. Thirty-one respondents (72.1%) had active patients with neuroendocrine tumors at the time of the survey. Thirty-one respondents (73.8%) indicated that GI NET was the most common NET in their practice. Six respondents (14.3%) selected lung and two (4.8%) selected gynecological NETs as the most common NETs in their practices. This is the first study to address the potential burden of NETs in the UAE. More education for family physicians, endocrinologists, and gastroenterologists in the UAE is needed to facilitate early diagnosis.

E. Senkus

Remy Peyret, Duaa alSaeed, Fouad Khelifi et al.

Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone, resulting in high intra and interobserver variability, which affects diagnosis reliability. This study aims to develop an automatic computerized system for diagnosing colorectal and prostate tumors by using images of biopsy samples to reduce time and diagnosis error rates associated with human analysis. We propose a CNN model for classifying colorectal and prostate tumors from multispectral images of biopsy samples. The key idea was to remove the last block of the convolutional layers and halve the number of filters per layer. Our results showed excellent performance, with an average test accuracy of 99.8% and 99.5% for the prostate and colorectal data sets, respectively. The system showed excellent performance when compared with pretrained CNNs and other classification methods, as it avoids the preprocessing phase while using a single CNN model for classification. Overall, the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images. The proposed CNN was detailed and compared with previously trained network models used as feature extractors. These CNNs were also compared with other classification techniques. As opposed to pretrained CNNs and other classification approaches, the proposed CNN yielded excellent results. The computational complexity of the CNNs was also investigated, it was shown that the proposed CNN is better at classifying images than pretrained networks because it does not require preprocessing. Thus, the overall analysis was that the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images.

Omid Bazgir, Zichen Wang, Ji Won Park et al.

In anti-cancer drug development, a major scientific challenge is disentangling the complex relationships between high-dimensional genomics data from patient tumor samples, the corresponding tumor's organ of origin, the drug targets associated with given treatments and the resulting treatment response. Furthermore, to realize the aspirations of precision medicine in identifying and adjusting treatments for patients depending on the therapeutic response, there is a need for building tumor dynamic models that can integrate both longitudinal tumor size as well as multimodal, high-content data. In this work, we take a step towards enhancing personalized tumor dynamic predictions by proposing a heterogeneous graph encoder that utilizes a bipartite Graph Convolutional Neural network (GCN) combined with Neural Ordinary Differential Equations (Neural-ODEs). We applied the methodology to a large collection of patient-derived xenograft (PDX) data, spanning a wide variety of treatments (as well as their combinations) on tumors that originated from a number of different organs. We first show that the methodology is able to discover a tumor dynamic model that significantly improves upon an empirical model which is in current use. Additionally, we show that the graph encoder is able to effectively utilize multimodal data to enhance tumor predictions. Our findings indicate that the methodology holds significant promise and offers potential applications in pre-clinical settings.

Yiding Zhang, Zhixing Xu, Hui Quan et al.

The primary objective of phase I oncology studies is to establish the safety profile of a new treatment and determine the maximum tolerated dose (MTD). This is motivated by the development of cytotoxic agents based on the underlying assumption that the higher the dose, the greater the likelihood of efficacy and toxicity. However, evidence from the recent development of cancer immunotherapies that aim to stimulate patients' immune systems to fight cancer challenges this assumption, particularly further escalation after a certain dose level might not necessarily increase the efficacy. Dose escalation study of molecular targeted agents (MTA) often does not only rely on the safety profile. In this paper, we propose a simple and flexible model that uses multivariate Gaussian latent variables to integrate toxicity endpoint and efficacy biomarker. This model can be easily extended to incorporate additional immune biomarkers. By simultaneously considering multiple outcomes, the proposed method is better at identifying the biologically optimal dose, which results in better decision-making. Simulation studies showed that the proposed method has desirable operating characteristics by determining the target dose with an optimal risk-benefit trade-off. We have also implemented our proposed method in a user-friendly R Shiny tool.

Chockalingam Senthilnathan, Tal Cohen

There is significant interest in modelling the mechanics and physics of growth of soft biological systems such as tumors and bacterial biofilms. Solid tumors account for more than 85% of cancer mortality and bacterial biofilms account for a significant part of all human microbial infections.These growing biological systems are a mixture of fluid and solid components and increase their mass by intake of diffusing species such as fluids and nutrients (swelling) and subsequent conversion of some of the diffusing species into solid material (growth). Experiments indicate that these systems swell by large amounts and that the swelling and growth are intrinsically coupled. However, many existing theories for swelling coupled growth employ linear poroelasticity, which is limited to small swelling deformations, and employ phenomenological prescriptions for the dependence of growth rate on concentration of diffusing species and the stress-state in the system. In particular, the termination of growth is enforced through the prescription of a critical concentration of diffusing species and a homeostatic stress. In contrast, by developing a fully coupled swelling-growth theory that accounts for large swelling through nonlinear poroelasticity, we show that the emergent driving stress for growth automatically captures all the above phenomena. Further, we show that for the soft growing systems considered here, the effects of the homeostatic stress and critical concentration can be encapsulated under a single notion of a critical swelling ratio. The applicability of the theory is shown by its ability to capture experimental observations of growing tumors and biofilms under various mechanical and diffusion-consumption constraints. Additionally, compared to generalized mixture theories, our theory is amenable to relatively easy numerical implementation with a minimal physically motivated parameter space.

Lukas Hirsch, Yu Huang, Hernan A. Makse et al.

Women with an increased life-time risk of breast cancer undergo supplemental annual screening MRI. We propose to predict the risk of developing breast cancer within one year based on the current MRI, with the objective of reducing screening burden and facilitating early detection. An AI algorithm was developed on 53,858 breasts from 12,694 patients who underwent screening or diagnostic MRI and accrued over 12 years, with 2,331 confirmed cancers. A first U-Net was trained to segment lesions and identify regions of concern. A second convolutional network was trained to detect malignant cancer using features extracted by the U-Net. This network was then fine-tuned to estimate the risk of developing cancer within a year in cases that radiologists considered normal or likely benign. Risk predictions from this AI were evaluated with a retrospective analysis of 9,183 breasts from a high-risk screening cohort, which were not used for training. Statistical analysis focused on the tradeoff between number of omitted exams versus negative predictive value, and number of potential early detections versus positive predictive value. The AI algorithm identified regions of concern that coincided with future tumors in 52% of screen-detected cancers. Upon directed review, a radiologist found that 71.3% of cancers had a visible correlate on the MRI prior to diagnosis, 65% of these correlates were identified by the AI model. Reevaluating these regions in 10% of all cases with higher AI-predicted risk could have resulted in up to 33% early detections by a radiologist. Additionally, screening burden could have been reduced in 16% of lower-risk cases by recommending a later follow-up without compromising current interval cancer rate. With increasing datasets and improving image quality we expect this new AI-aided, adaptive screening to meaningfully reduce screening burden and improve early detection.

Katherine Elizabeth Warren

Maria Elena Mantione, Ilenia Sana, Maria Giovanna Vilia et al.

Renal cell carcinoma is highly inflamed, and tumor cells are embedded into a microenvironment enriched with IL1. While inflammatory pathways are well characterized in the immune system, less is known about these same pathways in epithelial cells; it is unclear if and how innate immune signals directly impact on cancer cells, and if we could we manipulate these for therapeutic purposes. To address these questions, we first focused on the inflammatory receptors belonging to the IL1- and Toll-like receptor family including negative regulators in a small cohort of 12 clear cell RCC (ccRCC) patients’ samples as compared to their coupled adjacent normal tissues. Our data demonstrated that renal epithelial cancer cells showed a specific and distinctive pattern of inflammatory receptor expression marked by a consistent downregulation of the inhibitory receptor SIGIRR mRNA. This repression was confirmed at the protein level in both cancer cell lines and primary tissues. When we analyzed in silico data of different kidney cancer histotypes, we identified the clear cell subtype as the one where SIGIRR was mostly downregulated; nonetheless, papillary and chromophobe tumor types also showed low levels as compared to their normal counterpart. RNA-sequencing analysis demonstrated that IL1 stimulation of the ccRCC cell line A498 triggered an intrinsic signature of inflammatory pathway activation characterized by the induction of distinct “pro-tumor” genes including several chemokines, the autocrine growth factor IL6, the atypical co-transcription factor NFKBIZ, and the checkpoint inhibitor PD-L1. When we looked for the macroareas most represented among the differentially expressed genes, additional clusters emerged including pathways involved in cell differentiation, angiogenesis, and wound healing. To note, SIGIRR overexpression in A498 cells dampened IL1 signaling as assessed by a reduced induction of NFKBIZ. Our results suggest that SIGIRR downregulation unleashes IL1 signaling intrinsic to tumor cells and that manipulating this pathway may be beneficial in ccRCC.

Manasa Anipindi, Ryan J. Smith, Madiha Gilani

The incidence of cervical cancer has decreased in recent years due to widespread vaccination and routine screenings. It can be treated successfully, and the prognosis is also excellent if detected early. However, the 5-year survival rate for patients with stage IV cervical cancer is only 17% even with aggressive systemic chemotherapy. With the Food and Drug Administration (FDA)’s approval of immunotherapy, the prognosis has improved. We present a patient with stage IV cervical cancer who could not tolerate platinum-based chemotherapy and bevacizumab, so she was started on an immune checkpoint inhibitor, as her tumor was 100% programmed cell death ligand-1 (PD-L1) positive. She survived more than 2 years since the diagnosis of stage IV cervical cancer without any significant side effects. Based on our patient’s response, the use of immune checkpoint inhibitors as a single agent needs further research and probably can be considered in patients with stage 4 cervical cancer who cannot tolerate standard chemotherapy.

Seung-hwan Jeong, Jang Hee Han, Chang Wook Jeong et al.

Upper tract urothelial carcinoma (UTUC) occurs in urothelial cells from the kidney and the ureters. Carbohydrate antigen 19-9 (CA 19-9) is a tumor marker for pancreatic and gastrointestinal cancers, and its high levels are associated with poor prognosis in bladder cancer. In this study, prospective patients enrolled in the registry of Seoul National University were retrospectively examined to determine the clinical significance of CA 19-9 in UTUC. In 227 patients, high serum CA 19-9 levels reflected a high tumor burden represented by high T and N stages, leading to adverse prognosis in metastasis-free or overall survival. Subsequently, propensity score matching analysis showed that the CA 19-9 level is an independent prognostic factor of UTUC.