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

Tanya Zlatanova, Aynura Changalova, Urska Janzic et al.

Over the las0t decades, lung cancer treatment has improved immensely, mainly due to the incorporation of new targeted treatments and immunotherapy. A relatively new and potentially highly effective class of drugs, antibody-drug conjugates (ADCs), has been introduced to the clinical setting and is currently under intense investigation, alone and in combination with other molecules. This study aims to summarize the latest data on ADCs for lung cancer treatment and to analyze their potential, toxicity profile, and challenges.

Changchun Yang, Weiqian Dai, Yilan Zhang et al.

Chromosome analysis is vital for diagnosing genetic disorders and guiding cancer therapy decisions through the identification of somatic clonal aberrations. However, developing an AI model are hindered by the overwhelming complexity and diversity of chromosomal abnormalities, requiring extensive annotation efforts, while automated methods remain task-specific and lack generalizability due to the scarcity of comprehensive datasets spanning diverse resource conditions. Here, we introduce CHROMA, a foundation model for cytogenomics, designed to overcome these challenges by learning generalizable representations of chromosomal abnormalities. Pre-trained on over 84,000 specimens (~4 million chromosomal images) via self-supervised learning, CHROMA outperforms other methods across all types of abnormalities, even when trained on fewer labelled data and more imbalanced datasets. By facilitating comprehensive mapping of instability and clonal leisons across various aberration types, CHROMA offers a scalable and generalizable solution for reliable and automated clinical analysis, reducing the annotation workload for experts and advancing precision oncology through the early detection of rare genomic abnormalities, enabling broad clinical AI applications and making advanced genomic analysis more accessible.

Joshua P Schiff, Beatriz Guevara, Amir Ahari et al.

Abstract Background Adaptive radiotherapy (ART) is an advanced form of image-guided radiotherapy that involves the re-contouring and re-planning of a patient’s treatment plan, either while the patient is on the table (online) or in between fractions (offline). ART allows for the adjustment of a treatment plan to respect a patient’s changes in internal anatomy, something that is critical in the treatment of gastrointestinal (GI) malignancies in which the mobile and radiosensitive GI tract plays a key role in driving toxicity. Herein we review the indications for both online and offline ART in GI cancers. Main text Online ART plays a critical role in the treatment of pancreatic cancer when using stereotactic body radiotherapy (SBRT). A variety of ART workflows have demonstrated that ART allows for the safe dose-escalated treatment of locally advanced pancreatic cancer. In addition to pancreatic cancer, there are now a bevy of data demonstrating that ART plays a key role in the treatment of liver cancers and abdominal oligometastases when using SBRT and allows for the safe delivery of single-fraction abdominal SBRT. While lower GI cancers are generally not treated with SBRT-like doses, both online and offline ART workflows have been shown to potentially reduce toxicity in patients with anal and rectal cancers. Improved integration of artificial intelligence and direct-to-unit workflows in ART hold promise that the overall process can become more efficient, allowing for more widespread adoption in GI radiation oncology. Conclusions ART is an expanding radiotherapy paradigm in which a patient’s treatment plan is adjusted to match observed changes in patient anatomy and has been successfully incorporated into the treatment of a variety of GI cancers. The successful implementation of workflows in pancreatic cancer, liver cancers, and lower GI cancers, amongst others, as well as incorporation into multi-center clinical trials, suggest that ART will continue to play a critical role of GI radiation oncology for years to come. As improvements in efficiency and access allow for increasing use of ART world-wide, we predict that ART will continue to play a critical part in the management of patients with GI malignancies.

Lian Fang MD, Zhiyu Zhang MD, Ouyang Song MD et al.

Introduction Sarcomatoid renal cell carcinoma (sRCC) is rare but highly aggressive and is associated with poor prognosis and limited treatment responsiveness. Despite several studies investigating its clinicopathological features, existing research is often limited by small sample sizes and short follow-up periods, and currently, no prognostic risk model is specific to patients with non-metastatic sRCC. This study aimed to investigate the clinicopathological characteristics of patients with non-metastatic sRCC and develop a predictive model for postoperative mortality risk. Methods In this retrospective study, we analyzed the clinical data of 45 patients diagnosed with non-metastatic sRCC who underwent surgical treatment at our institution's Department of Urology, between January 2008 and June 2024. These patients were compared with 527 patients with non-sarcomatoid renal cell carcinoma (non-sRCC). The primary endpoint was death, and the exact cause of death was recorded. Routine postoperative examinations and treatment details were documented through outpatient and inpatient electronic medical record systems. Results The results indicated significant differences in body mass index, hypertension, surgical approach, nephrectomy type, surgical duration, maximum tumor diameter, tumor necrosis, T stage, and Ki-67 expression between patients with sRCC and those with non-sRCC ( P  < 0.05). Survival analysis revealed that the cancer-specific survival (CSS) for patients with sRCC was significantly lower than that for patients with non-sRCC ( P  < 0.001). Cox univariate and multivariate analyses identified maximum pathological tumor diameter, T stage, and high Ki-67 expression as independent risk factors. Based on these factors, we developed a postoperative mortality risk prediction model for patients with sRCC, with the calibration curves demonstrating a good fit of the model. Conclusions The proposed model is designed for patients with non-metastatic sRCC. It has potential clinical application value, aiding in the identification of high-risk patients and providing guidance for individualized treatment and close follow-up.

Xiaomeng Di, Xiaohong Shi, Feng Gai et al.

BackgroundImmune checkpoint inhibitor (ICI)-associated neurological immune-related adverse events (NAEs) are rare but serious side effects, of which autoimmune encephalitis (AIE) is a potentially fatal central nervous system disorder requiring more attention.MethodsWe performed a retrospective disproportionality analysis of NAE reports in the FDA Adverse Event Reporting System (FAERS) and the Japanese Adverse Event Reporting Database (JADER) from 2004 to 2024, utilizing reporting odds ratio (ROR), proportional reporting ratio (PRR), the Bayesian confidence propagation neural network BCPNN, and the multi-item gamma Poisson shrinker (MGPS) for signal detection.ResultsIn total, 3,999 reports of ICI-associated NAEs were identified from the FAERS database, of which 1,998 reports were AIE. 1,558,251 reports of AEs were collected from the JADER database, which contained 890 AIE reports. ICIs, including pembrolizumab, nivolumab, atezolizumab, ipilimumab, and durvalumab, were identified among the top 30 agents in both databases, demonstrating significant signals across all 4 algorithms. Except for noninfectious myelitis, acute disseminated encephalomyelitis, and multiple sclerosis, positive signals were detected in all other preferred terms (PTs). These NAEs accounted for 23.7% of total mortality, with myasthenia gravis (MG) exhibiting the highest mortality rate at 30.63%. Specific PTs, such as aseptic meningitis, AIE, chronic inflammatory demyelinating polyradiculoneuropathy, Guillain-Barré syndrome, MG, myelitis, and immune-related myopathy, were associated with the severity of outcomes, showing significant statistical differences between severe and non-severe cases (p &lt; 0.05).ConclusionOur study found a notable correlation between ICIs and AIE and other specific NAEs, highlighting the demographic characteristics, time to onset, and disease severity of ICI-induced NAEs, thereby facilitating the timely recognition and treatment of these ICI therapy-related complications.

G.O.L. Ochoa Leon, R. Carvajal García, J.N. Sayeg Lozano et al.

Maria Frances Coakley, Javier Pascual, Seamus O’Reilly

F. Edward Boas

Jiaying Lai, Yunzhou Liu, Robert B. Scharpf et al.

Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.

Urban Železnik, Matej Krajnc, Tanmoy Sarkar

One of the hallmarks of pre-migratory tumors is the progressive loss of compact morphology. To investigate how tumors may intrinsically regulate their shape during growth, we employ a three-dimensional (3D) vertex model of multicellular aggregates that incorporates key structural features of tumor spheroids, including its surface, a proliferative rim, and a necrotic core. Focusing exclusively on tumor-intrinsic mechanical interactions, we examine how their collective effects guide morphological evolution en route to metastasis. We show that spheroids acquire lobulated morphologies through an interplay between differential tensions at the spheroid surface and the living-necrotic interface (LNI), together with differential growth within the proliferative rim. In addition, spheroid shapes can be substantially modulated by tissue rheological properties emerging from active, cell-scale forces. Our cell- and tissue-scale simulations of tumor morphologies are enabled by a computational framework that overcomes a major limitation of 3D vertex models - the lack of cell-division - by introducing a graph-based polyhedral-division algorithm within the Graph Vertex Model (GVM).

Lina Buitrago, Juan Sosa, Cristian Gonzáles

In this paper, we compare semiparametric and parametric model adjustments for cancer mortality in breast and cervical cancer and prostate and lung cancer in men, according to age and period of death. Semiparametric models were adjusted for the number of deaths from the two localizations of greatest mortality by sex: breast and cervix in women; prostate and lungs in men. Adjustments in different semiparametric models were compared; which included making adjustments with different distributions and variable combinations in the parametric and non-parametric part, for localization as well as for scale. Finally, the semiparametric model with best adjustment was selected and compared to traditional model; that is, to the generalized lineal model with Poisson response and logarithmic link. Best results for the four kinds of cancer were obtained for the selected semiparametric model by comparing it to the traditional Poisson model based upon AIC, envelope correlation between estimated logarithm rate and real rate logarithm. In general, we observe that in estimation, rate increases with age; however, with respect to period, breast cancer and stomach cancer in men show a tendency to rise over time; on the other hand, for cervical cancer, it remains virtually constant, but for lung cancer in men, as of 2007, it tends to decrease.

Soong June Bae, Jung Whan Chun, Sae Byul Lee et al.

Abstract Background This study investigated the feasibility of sentinel lymph node biopsy (SLNB) after neoadjuvant systemic therapy (NAST) in patients with initially high nodal burden. Methods In the multicenter retrospective cohort, 388 individuals with cN1–3 breast cancer who underwent NAST and had SLNB followed by completion axillary lymph node dissection were included. In an external validation cohort, 267 patients with HER2+ or triple-negative breast cancer (TNBC) meeting similar inclusion criteria were included. Primary outcome was the false-negative rates (FNRs) of SLNB according to the MRI response and subtypes. We defined complete MRI responders as patients who experienced disappearance of suspicious features in the breast and axilla after NAST. Results In the multicenter retrospective cohort, 130 (33.5%) of 388 patients were of cN2-3, and 55 (14.2%) of 388 patients showed complete MRI responses. In hormone receptor-positive HER2− (n = 207), complete and non-complete responders had a high FNRs (31.3% [95% CI 8.6–54.0] and 20.9% [95% CI 14.1–27.6], respectively). However, in HER2+ or TNBC (n = 181), the FNR of complete MRI responders was 0% (95% CI 0–0), whereas that of non-complete responders was 33.3% (95% CI 20.8–45.9). When we validated our findings in the external cohort with HER2+ or TNBC (n = 267), of which 34.2% were cN2-3, the FNRs of complete were 7.1% (95% CI 0–16.7). Conclusions Our findings suggest that SLNB can be a reliable option for nodal status evaluation in selected patients who have responded well to NAST, especially in HER2+ and TNBC patients who show a complete MRI response.

Chunqiu Liu, Honglei Guo, Fangzhou Jin

BackgroundStomach cancer is considered the fifth most common cancer worldwide. This study utilized bibliometric analysis to construct a visualization map of the relationship between stomach cancer and exosomes, aiming to reveal research trends and emerging themes, and provide direction for future research.MethodRetrieve relevant literature on gastric cancer exosomes in the Web of Science Core Collection (WoSCC) over the past 25 years according to search criteria, and conduct bibliometric and visualization analysis using bibliometric software VOSviewer and CiteSpace.ResultsThis study included a total of 727 articles, with an overall increasing trend in annual publication output. There were 68 countries involved, with China having the largest number of publications followed by the United States. A total of 957 research institutions were involved, with most of the top 10 institutions in terms of publication output being universities in China. The top 5 journals are Molecular Cancer, Cell death &amp; disease, Cancers, International journal of molecular sciences, and Frontiers in oncology. A total of 4529 authors were involved, with 5 authors having a publication output of no less than 13 articles. A total of 35516 references were cited, with a total number of citations. The top publication is “Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells”.ConclusionOver the past 25 years, researchers have been dedicated to studying the field of exosomes related to gastric cancer, and research in this area is currently progressing steadily. Based on previous studies, exosomes in gastric adenocarcinoma serve as biomarkers, potential therapeutic targets, and post-resistance treatment, which represents current hotspots and emerging frontiers in research.

Jakub Grzeszczyk, Michał Karwatowski, Daria Łukasik et al.

This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine. Eleven cell types were used directly and indirectly in the experiments, including damaged and unrecognized categories. The deep learning models employed in the system achieve a high score of average precision and recall metrics, i.e. 0.94 and 0.8 respectively, for the selected three types of tumors. This variety of label types allowed us to draw a meaningful conclusion that there are relatively few mistakes for tumor cell types. Additionally, the model learned tumor cell features well enough to avoid misclassification mistakes of one tumor type into another. The experiments also revealed that the quality of the results improves with the dataset size (excluding the damaged cells). It is worth noting that all the experiments were done using a custom dedicated dataset provided by the cooperating vet doctors.

Xiangyu Meng, Xue Li, Qing Yang et al.

Benefiting from the advancements in deep learning, various genomic analytical techniques, such as survival analysis, classification of tumors and their subtypes, and exploration of specific pathways, have significantly enhanced our understanding of the biological mechanisms driving cancer. However, the overfitting issue, arising from the limited number of patient samples, poses a challenge in improving the accuracy of genome analysis by deepening the neural network. Furthermore, it remains uncertain whether novel approaches such as the sparsely gated mixture of expert (MOE) and self-attention mechanisms can improve the accuracy of genomic analysis. In this paper, we introduce a novel sparsely gated RNA-seq analysis framework called Gene-MOE. This framework exploits the potential of the MOE layers and the proposed mixture of attention expert (MOAE) layers to enhance the analysis accuracy. Additionally, it addresses overfitting challenges by integrating pan-cancer information from 33 distinct cancer types through pre-training.We pre-trained Gene-MOE on TCGA pan-cancer RNA-seq dataset with 33 cancer types. Subsequently, we conducted experiments involving cancer classification and survival analysis based on the pre-trained Gene-MOE. According to the survival analysis results on 14 cancer types, Gene-MOE outperformed state-of-the-art models on 12 cancer types. Through detailed feature analysis, we found that the Gene-MOE model could learn rich feature representations of high-dimensional genes. According to the classification results, the total accuracy of the classification model for 33 cancer classifications reached 95.8%, representing the best performance compared to state-of-the-art models. These results indicate that Gene-MOE holds strong potential for use in cancer classification and survival analysis.

Hayden Gunraj, Chi-en Amy Tai, Alexander Wong

The recent introduction of synthetic correlated diffusion (CDI$^s$) imaging has demonstrated significant potential in the realm of clinical decision support for prostate cancer (PCa). CDI$^s$ is a new form of magnetic resonance imaging (MRI) designed to characterize tissue characteristics through the joint correlation of diffusion signal attenuation across different Brownian motion sensitivities. Despite the performance improvement, the CDI$^s$ data for PCa has not been previously made publicly available. In our commitment to advance research efforts for PCa, we introduce Cancer-Net PCa-Data, an open-source benchmark dataset of volumetric CDI$^s$ imaging data of PCa patients. Cancer-Net PCa-Data consists of CDI$^s$ volumetric images from a patient cohort of 200 patient cases, along with full annotations (gland masks, tumor masks, and PCa diagnosis for each tumor). We also analyze the demographic and label region diversity of Cancer-Net PCa-Data for potential biases. Cancer-Net PCa-Data is the first-ever public dataset of CDI$^s$ imaging data for PCa, and is a part of the global open-source initiative dedicated to advancement in machine learning and imaging research to aid clinicians in the global fight against cancer.

Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin

Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets is arduous and time-consuming. Thus, the development of a reliable deep learning (DL) model is essential to enhance diagnostic accuracy and ultimately save lives. This study introduces an innovative optimization-based deep ensemble approach employing transfer learning (TL) to efficiently classify brain tumors. Our methodology includes meticulous preprocessing, reconstruction of TL architectures, fine-tuning, and ensemble DL models utilizing weighted optimization techniques such as Genetic Algorithm-based Weight Optimization (GAWO) and Grid Search-based Weight Optimization (GSWO). Experimentation is conducted on the Figshare Contrast-Enhanced MRI (CE-MRI) brain tumor dataset, comprising 3064 images. Our approach achieves notable accuracy scores, with Xception, ResNet50V2, ResNet152V2, InceptionResNetV2, GAWO, and GSWO attaining 99.42%, 98.37%, 98.22%, 98.26%, 99.71%, and 99.76% accuracy, respectively. Notably, GSWO demonstrates superior accuracy, averaging 99.76\% accuracy across five folds on the Figshare CE-MRI brain tumor dataset. The comparative analysis highlights the significant performance enhancement of our proposed model over existing counterparts. In conclusion, our optimized deep ensemble model exhibits exceptional accuracy in swiftly classifying brain tumors. Furthermore, it has the potential to assist neurologists and clinicians in making accurate and immediate diagnostic decisions.

Priya Gupta, Vishakha Kalikar, Roy Patankar et al.

Mirizzi syndrome was previously considered an absolute contraindication for laparoscopic cholecystectomy. However, with advances in radiology and increasing familiarity with the pathophysiology, the successful laparoscopic management of Mirizzi syndrome is now increasingly reported. The presence of cirrhosis and periportal collaterals increases the difficulty of performing laparoscopic cholecystectomy. Intraoperative indocyanine green(ICG) imaging is very helpful in these complex situations. We present the first published report of ICG-assisted laparoscopic cholecystectomy in type 1 Mirizzi syndrome with Child-Pugh A cirrhosis.

Dominik T. Schneider

Keerti Sitani, Sandip Basu

Esophageal neuroendocrine neoplasms (NENs) are uncommon type of esophageal malignancies. We describe the clinical course and molecular imaging features of the relatively rare esophageal malignancy (an aggressive poorly differentiated NEN) that was widely metastatic at the initial presentation. The patient underwent multiple cycles of chemotherapeutic regimens, employing cisplatin-etoposide and nanopaclitaxel-carboplatin and later on rechallenge with cisplatin-etoposide. There was observation of fluctuating disease course and differential characteristics of tumor lesions in terms of treatment response and recurrences with multiple cycles of chemotherapy. In view of the histopathology of high Mib-1 labeling index and dual tracer positron emission tomography-computed tomography (PET-CT) (flurodeoxyglucose [FDG] and 68Ga-DOTATATE) features, the patient was not a suitable candidate for 177Lu-DOTATATE PRRT and FDG PET-CT was the preferred imaging modality for both treatment response assessment and disease monitoring in this patient. The varying response among metastatic lesions in the same individual (with one lesion showing partial response and the other one demonstrating disease progression) was an additional noteworthy feature of the case.