Divine Darlington Logo, Prakash B. Kodali, Judith Anaman-Torgbor
et al.
Introduction
Tobacco use among adolescents is a concern in the Upper East Region
of Ghana. We estimated the prevalence and identified factors contributing to
single and multiple use of tobacco products among junior high school students
in Ghana.
Methods
We conducted a cross-sectional analysis of a baseline survey of a schoolbased
tobacco control intervention among adolescents in the Upper East Region of
Ghana in 2022. A multi-stage cluster sampling approach was employed to identify
the study sample, and data were collected using self-administered questionnaires.
Current use of single tobacco products (at least one: cigarette, e-cigarette, shisha,
or smokeless tobacco products) and multiple products (≥2 products) in the past
30 days was assessed. Multinomial logistic regression was used to assess the
association of sociodemographic characteristics, perceptions towards tobacco’s
health risks, and exposure to tobacco products with single and multiple product
use. Adjusted relative risk ratios (ARRR) and their corresponding 95% confidence
intervals (CI) were computed.
Results
We surveyed 1328 adolescents, comprising an equal proportion of males
(49.8%) and females (50.4%). One in five (21.7%) reported using tobacco
products, with 11.5% using single products and 13.0% using multiple products.
Shisha (13.6%), cigarettes (10.6%), e-cigarettes (8.2%), and smokeless tobacco
(6.0%) were used. A number of factors were identified to be associated with
tobacco use among adolescents.
Conclusions
One in five junior high school students used at least one form of
tobacco product. Adolescent tobacco use is impacted by demographic factors
and risk perceptions. Further studies are needed to better understand these
associations.
Diseases of the respiratory system, Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Introduction: Bone metastases represent the second most common site of distant spread in differentiated thyroid cancer and are associated with a significantly poorer prognosis than lymph node or pulmonary metastases. In this context, poorly differentiated thyroid carcinomas generally show heterogeneous uptake on iodine-131 scintigraphy and on [¹⁸F]FDG PET/CT. The optimal therapeutic strategy for oligometastatic disease remains poorly defined, lying between systemic treatments (iodine-131 therapy) and localized approaches such as surgery or stereotactic radiotherapy. Clinical Case: We report the case of a 54-year-old woman followed for papillary thyroid carcinoma. Post-therapeutic scintigraphy performed after administration of 100 mCi of radioactive iodine revealed a bone uptake focus in the mid-third of the left femur, suggestive of metastasis. The post-radioiodine therapy assessment showed a marked increase in serum thyroglobulin levels, reaching 1960 ng/mL. [¹⁸F]FDG PET/CT demonstrated a single, intensely hypermetabolic bone lesion extending from the mid- to the distal third of the left femoral diaphysis. Postoperative evolution was remarkable, with a spectacular drop in thyroglobulin levels following complete surgical excision performed with curative intent. Conclusion: [¹⁸F]FDG PET/CT is an essential tool in the diagnostic and therapeutic evaluation of poorly differentiated follicular-origin thyroid carcinomas. Its contribution is pivotal in guiding clinical decision-making. Moreover, surgical management of isolated bone metastases can offer a genuine opportunity for durable disease control. ----------------------------------------------------------------- Introduction Thyroid cancer is the most common malignant endocrine tumor worldwide. Histologically, differentiated thyroid carcinoma of follicular origin (DTC), which develops from the epithelial cells of the thyroid gland, is the most frequent subtype (1). In the 5th edition of the World Health Organization (WHO) classification of thyroid tumors, these neoplasms are categorized according to their pathological features, molecular profile, and biological behavior (2). Follicular carcinomas, papillary carcinomas, and clear cell ovarian carcinomas are traditionally grouped under well-differentiated thyroid carcinomas and are distinguished from other, less differentiated types. In this same edition, a new category of non-anaplastic high-grade follicular-derived carcinomas was introduced. It includes poorly differentiated thyroid carcinoma and high-grade differentiated thyroid carcinomas, characterized by tumor necrosis and/or increased mitotic activity, with an intermediate prognosis between well-differentiated and undifferentiated carcinomas (2). Distant metastases are a rare but unfavorable prognostic event in DTC, with a prevalence of approximately 5% (3). Bone metastases represent the second most common site of distant spread in differentiated thyroid cancer (4) and are associated with a significantly poorer prognosis than lymph node or pulmonary metastases (5). Iodine-131 scintigraphy ([¹³¹I]I WBS) plays an essential role in the management of patients with DTC. It is used for postoperative assessment of residual thyroid tissue, detection of distant metastases, determination of eligibility for radioactive iodine therapy, and evaluation of treatment response (6). During the dedifferentiation process, thyroid cancer cells progressively lose their ability to uptake iodine and to organize functionally, which significantly limits the use of this radioisotope—not only for diagnosis but also for therapy—thus complicating patient management (7). The introduction of positron emission tomography combined with computed tomography (PET/CT) has profoundly transformed the management of cancer patients. Among the various radiotracers, fluorodeoxyglucose ([18F]FDG) is the most widely used, and its clinical value has been confirmed by numerous studies, particularly in patients with follicular-derived thyroid carcinoma, including poorly differentiated and undifferentiated subtypes (8, 9). Well-differentiated follicular-origin thyroid carcinomas, without high-grade features, typically show strong uptake of radioactive iodine and low uptake of [18F]FDG. Conversely, dedifferentiated thyroid carcinomas are characterized by intense [18F]FDG uptake and lack of radioactive iodine uptake. Poorly differentiated thyroid carcinomas may exhibit heterogeneous uptake of both radiotracers (7). The optimal therapeutic strategy for oligometastatic disease lies between systemic treatments (radioiodine therapy) and localized approaches (surgery or stereotactic radiotherapy). We report the case of a poorly differentiated thyroid carcinoma that presented a remarkable biochemical response after local treatment of an isolated femoral metastasis. Clinical Observation This is a 54-year-old female patient followed for papillary thyroid carcinoma diagnosed fourteen years earlier. The initial treatment consisted of a left lobectomy–isthmectomy performed for diagnostic and therapeutic purposes, which concluded with a diagnosis of papillary microcarcinoma. Postoperative evolution under suppressive therapy was favorable, with no evidence of local or metastatic recurrence for several years. During follow-up, biological tests revealed an elevation in thyroglobulin levels. At the same time, cervical ultrasound showed nodular changes classified as EU-TIRADS IV in the right thyroid lobe, with features suggestive of a suspicious contralateral thyroid carcinoma. The patient subsequently underwent a complementary right lobectomy–isthmectomy, resulting in a total thyroidectomy. Histopathological examination of the right surgical specimen revealed a 1.3-cm papillary thyroid carcinoma of the follicular variant, associated with a 4-mm papillary microcarcinoma, with no vascular emboli, no capsular invasion, nor lymph node involvement. Given the histological subtype, the multifocal nature of the tumor, and the intermediate risk of recurrence, adjuvant radioactive iodine (¹³¹I) therapy was indicated in accordance with international recommendations. The patient thus received a course of radioiodine therapy under TSH stimulation. Post-therapeutic scintigraphy showed residual thyroid uptake associated with a focus of osseous uptake in the mid-shaft of the left femur, consistent with a metastatic lesion (Figure 1). The post-therapy assessment revealed a marked elevation of serum thyroglobulin, reaching 1960 ng/mL, with suppressed TSH and absence of anti-thyroglobulin antibodies. In this context, an [18F]FDG PET/CT was performed in accordance with the American Thyroid Association (ATA) recommendations. The examination demonstrated a single, intensely hypermetabolic osseous lesion extending from the mid-shaft to the distal third of the left femoral diaphysis (Figure 2). The lesion was intramedullary, associated with medullary expansion and cortical thinning, without overt bone lysis or pathological fracture. No additional pathological uptake was identified (Figure 2). The isolated bone lesion prompted presentation of the case at a multidisciplinary orthopedic oncology and nuclear medicine tumor board. Given the solitary and accessible nature of the lesion, a complete curative-intent surgical excision was recommended. The procedure was performed without complication and allowed an en bloc resection of the femoral lesion while preserving bone stability. Histopathological analysis of the surgical specimen confirmed the metastatic nature of the tumor, corresponding to a poorly differentiated thyroid carcinoma derived from a papillary carcinoma. Postoperative evolution was marked by a dramatic drop in serum thyroglobulin from 1960 ng/mL to 2.2 ng/mL, indicating a complete biochemical response. No evidence of local or metastatic recurrence was observed on the six-month follow-up imaging. Discussion Differentiated follicular thyroid carcinoma (DTC) is one of the most curable cancers (10). DFTCs are characterized by a slowly progressive evolution and show a 10-year survival rate of 90% (4). However, the occurrence of distant metastases reduces this rate to 40% (11). Age, sex, and the involvement of multiple organs are independent factors associated with mortality in patients with DTC. Patients with DTC and bone metastases have a poor prognosis, with 10-year survival rates ranging from 0 to 34% (12). Bone metastases from DTC are resistant to radioactive iodine therapy (10, 13), the reference treatment for metastases particularly in vital organs—arising from differentiated thyroid cancer (DTC). Surgical resection is generally recommended for isolated, solitary, and accessible metastases (14), and it is associated with a significant improvement in survival (15). However, in patients with multifocal disease, the role of surgical resection is less clearly defined. Local treatment of bone metastases is recognized as a significant factor in improving survival rates (16). Similarly, guide-lines specify that complete resection of isolated bone metastases may prolong overall survival. In the patient presented in this case, surgical excision led to an almost complete decrease in thyroglobulin levels, reflecting an excellent therapeutic response. This marked decrease represents a major biological indicator of treatment effectiveness, suggesting a significant reduction in residual tumor tissue and confirming the relevance of the surgical strategy adopted. Conclusion [18F]FDG PET/CT is a valuable tool in the diagnosis and treatment of differentiated follicular thyroid carcinoma. Its impact on clinical management is tangible. Surgical management of isolated bone metastases, although rare, can offer curative potential or durable disease control. This case illustrates in an exemplary manner the relevance of [18F]FDG PET/CT in the management of poorly differentiated thyroid carcinomas refractory to radioactive iodine.
Guannan Gong, Satrajit Roychoudhury, Allison Meisner
et al.
Designing modern oncology trials requires synthesizing evidence from prior studies to inform hypothesis generation and sample size determination. Trial designs based on incomplete or imprecise summaries can lead to misspecified hypotheses and underpowered studies, resulting in false positive or negative conclusions. To address this challenge, we developed LEAD-ONC (Literature to Evidence for Analytics and Design in Oncology), an AI-assisted framework that transforms published clinical trial reports into quantitative, design-relevant evidence. Given expert-curated trial publications that meet prespecified eligibility criteria, LEAD-ONC uses large language models to extract baseline characteristics and reconstruct individual patient data from Kaplan-Meier curves, followed by Bayesian hierarchical modeling to generate predictive survival distributions for a prespecified target trial population. We demonstrate the framework using five phase III trials in first-line non-small-cell lung cancer evaluating PD-1 or PD-L1 inhibitors with or without CTLA-4 blockade. Clustering based on baseline characteristics identified three clinically interpretable populations defined by histology. For a prospective randomized trial in the mixed-histology population comparing mono versus dual immune checkpoint inhibition, LEAD-ONC projected a modest median overall survival difference of 2.8 months (95 percent credible interval -2.0 to 7.6) and an estimated probability of at least a 3-month benefit of approximately 0.45. As LEAD-ONC remains under active development, these results are intended as preliminary demonstrations of the frameworks potential to support evidence-driven oncology trial design rather than definitive clinical conclusions.
Introduction: About 7–15% of patients with leukemia or myelodysplastic syndromes (MDS) may harbor germline mutations, even in the absence of a clear family history. According to current guidelines, germline testing is recommended in specific clinical scenarios, including early-onset MDS (before age 40), family history of hematologic or solid tumors, syndromic features (e.g., lymphedema, organ dysfunction), personal history of bone marrow failure (BMF) or inherited thrombocytopenia, suggestive cytogenetic findings (Gachard et al., 2023), or high variant allele frequency mutations (VAF >30%) (Kraft & Godley, 2020). Well-established predisposition syndromes include mutations in genes such as DDX41, RUNX1, ETV6, ANKRD26, GATA2, and inherited BMF syndromes. Identifying germline mutations is essential, as this can affect treatment decisions, stem cell donor selection, and genetic counseling. Skin fibroblast culture from biopsy is a reliable source of uncontaminated DNA for germline analysis (Cazzola, 2023), though the process is time-consuming, adding approximately 28 days to testing (Lia DeRoin et al., 2022), and is not widely available. The aim of this study is to evaluate the clinical indications for germline testing and to analyze the molecular findings in a tertiary oncology and hematology laboratory in Brazil. Methods: A retrospective chart review was conducted. Since the implementation of skin fibroblast culture in 2021, 29 samples were obtained; 22 met inclusion criteria (patients >18 years with DNA for germline testing). Clinical indications were reviewed by ELN 2022 criteria for suspected predisposition. DNA from cultured fibroblasts was analyzed by hereditary myeloid panel (HMP), targeted variant testing (TVT), or whole exome sequencing (WES). Results: Among the 22 patients, 4 (18%) had a personal history of ≥2 malignancies, including at least one hematologic cancer; 3 (13.6%) had both personal and family history of hematologic malignancies; 6 (27.2%) had deleterious variants on somatic panels suggestive of germline origin; and 6 (27.2%) had hematologic malignancies diagnosed at a younger-than-expected age. Two patients met 2 of these criteria, and 1 patient formally met none but had 1q gain and borderline age, prompting testing. All fibroblast cultures yielded sufficient DNA for molecular analysis. Of the 22 cases, 11 underwent testing with a 29-gene HMP, 5 with targeted testing (MLH1, DDX41, TP53, or ETV6), 3 with WES, and 3 with an expanded 136-gene panel. Germline predisposition was confirmed in 3 patients (13%). One MDS case diagnosed before age 40 showed a pathogenic GATA2 variant [c.1017+572C>T:p(?)] identified on the HMP; one AML case with a somatic DDX41 variant (c.346G>T) had germline confirmation by TVT, classified as likely pathogenic by ACMG criteria; and one T-prolymphocytic leukemia (T-PLL) case with family history of breast and colorectal cancer showed a pathogenic MLH1 variant (c.1731G>A) on TVT. Other tests were either negative or identified variants of uncertain significance or unknown relevance to hematologic disease. Discussion: Twenty-one of 22 patients were classified into at least one of the ELN 2022 germline suspicion criteria. While indications for germline testing are better defined in myeloid neoplasms, four patients had lymphoid malignancies (2 B-ALL, 1 mantle cell lymphoma, 1 T-PLL). Two of the 3 positive cases underwent targeted testing based on strong suspicion of germline origin from somatic findings, demonstrating the utility and lower cost of focused testing when appropriate. The third had a somatic RUNX1 mutation but a germline GATA2 variant, highlighting the importance of careful test selection. In the absence of specific guidelines for which genes to test in each case, broader panels may increase diagnostic yield. Among 8 patients with potentially germline variants identified in tumor sequencing, 2 were confirmed germline and 6 were tested for the relevant gene and effectively excluded. Conclusion: Germline testing in hematologic malignancies remains complex, due to evolving knowledge of clinical indicators and implicated genes. Increased case reporting and systematic screening may improve recognition of clinical and molecular clues. Establishing fibroblast culture as a standard method for germline testing and expanding understanding of gene-specific indications are essential steps toward accurate diagnosis, tailored treatment, and appropriate family counseling.
Phase I dose escalation trials in oncology generally aim to find the maximum tolerated dose (MTD). However, with the advent of molecular targeted therapies and antibody drug conjugates, dose limiting toxicities are less frequently observed, giving rise to the concept of optimal biological dose (OBD), which considers both efficacy and toxicity. The Estimand framework presented in the addendum of the ICH E9(R1) guidelines strengthens the dialogue between different stakeholders by bringing in greater clarity in the clinical trial objectives and by providing alignment between the targeted estimand under consideration and the statistical analysis methods. However, there lacks clarity in implementing this framework in early phase dose optimization studies. This manuscript aims at discussing the Estimand framework for dose optimization trials in oncology considering efficacy and toxicity through utility functions. Such trials should include Pharmacokinetics (PK) data, toxicity data, and efficacy data. Based on these data, the analysis methods used to identify the optimized dose/s are also described. Focusing on optimizing the utility function to estimate the OBD, the population-level summary measure should reflect only the properties used for the estimating this utility function. A detailed strategy recommendation for intercurrent events has been provided using a real-life oncology case study. Key recommendations regarding the estimand attributes include that in a seamless Phase I/II dose optimization trial, the treatment attribute should start when the subject receives the first dose. We argue that such a framework brings in additional clarity to dose optimization trial objectives and strengthens the understanding of the drug under consideration that would enable the correct dose to move to Phase II of clinical development.
Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilities for more precise and intelligent tumor screening and diagnosis. However, the progress is heavily hampered by the scarcity of annotated datasets, which demands extensive annotation efforts by radiologists. To tackle this challenge, we introduce FreeTumor, an innovative Generative AI (GAI) framework to enable large-scale tumor synthesis for mitigating data scarcity. Specifically, FreeTumor effectively leverages a combination of limited labeled data and large-scale unlabeled data for tumor synthesis training. Unleashing the power of large-scale data, FreeTumor is capable of synthesizing a large number of realistic tumors on images for augmenting training datasets. To this end, we create the largest training dataset for tumor synthesis and recognition by curating 161,310 publicly available Computed Tomography (CT) volumes from 33 sources, with only 2.3% containing annotated tumors. To validate the fidelity of synthetic tumors, we engaged 13 board-certified radiologists in a Visual Turing Test to discern between synthetic and real tumors. Rigorous clinician evaluation validates the high quality of our synthetic tumors, as they achieved only 51.1% sensitivity and 60.8% accuracy in distinguishing our synthetic tumors from real ones. Through high-quality tumor synthesis, FreeTumor scales up the recognition training datasets by over 40 times, showcasing a notable superiority over state-of-the-art AI methods including various synthesis methods and foundation models. These findings indicate promising prospects of FreeTumor in clinical applications, potentially advancing tumor treatments and improving the survival rates of patients.
Effective communication is essential in cancer care, yet patients often face challenges in preparing for complex medical visits. We present an interactive, Retrieval-augmented Generation-assisted system that helps patients progress from uninformed to visit-ready. Our system adapts the Ottawa Personal Decision Guide into a dynamic retrieval-augmented generation workflow, helping users bridge knowledge gaps, clarify personal values and generate useful questions for their upcoming visits. Focusing on localized prostate cancer, we conduct a user study with patients and a clinical expert. Results show high system usability (UMUX Mean = 6.0 out of 7), strong relevance of generated content (Mean = 6.7 out of 7), minimal need for edits, and high clinical faithfulness (Mean = 6.82 out of 7). This work demonstrates the potential of combining patient-centered design with language models to enhance clinical preparation in oncology care.
Sai Venkatesh Chilukoti, Imran Hossen Md, Liqun Shan
et al.
Based on global genomic status, the cancer tumor is classified as Microsatellite Instable (MSI) and Microsatellite Stable (MSS). Immunotherapy is used to diagnose MSI, whereas radiation and chemotherapy are used for MSS. Therefore, it is significant to classify a gastro-intestinal (GI) cancer tumor into MSI vs. MSS to provide appropriate treatment. The existing literature showed that deep learning could directly predict the class of GI cancer tumors from histological images. However, deep learning (DL) models are susceptible to various threats, including membership inference attacks, model extraction attacks, etc. These attacks render the use of DL models impractical in real-world scenarios. To make the DL models useful and maintain privacy, we integrate differential privacy (DP) with DL. In particular, this paper aims to predict the state of GI cancer while preserving the privacy of sensitive data. We fine-tuned the Normalizer Free Net (NF-Net) model. We obtained an accuracy of 88.98\% without DP to predict (GI) cancer status. When we fine-tuned the NF-Net using DP-AdamW and adaptive DP-AdamW, we got accuracies of 74.58% and 76.48%, respectively. Moreover, we investigate the Weighted Random Sampler (WRS) and Class weighting (CW) to solve the data imbalance. We also evaluated and analyzed the DP algorithms in different settings.
AI-assisted imaging made substantial advances in tumor diagnosis and management. However, a major barrier to developing robust oncology foundation models is the scarcity of large-scale, high-quality annotated datasets, which are limited by privacy restrictions and the high cost of manual labeling. To address this gap, we present PASTA, a pan-tumor radiology foundation model built on PASTA-Gen, a synthetic data framework that generated 30,000 3D CT scans with pixel-level lesion masks and structured reports of tumors across ten organ systems. Leveraging this resource, PASTA achieves state-of-the-art performance on 45 of 46 oncology tasks, including non-contrast CT tumor screening, lesion segmentation, structured reporting, tumor staging, survival prediction, and MRI-modality transfer. To assess clinical applicability, we developed PASTA-AID, a clinical decision support system, and ran a retrospective simulated clinical trial across two scenarios. For pan-tumor screening on plain CT with fixed reading time, PASTA-AID increased radiologists' throughput by 11.1-25.1% and improved sensitivity by 17.0-31.4% and precision by 10.5-24.9%; additionally, in a diagnosis-aid workflow, it reduced segmentation time by up to 78.2% and reporting time by up to 36.5%. Beyond gains in accuracy and efficiency, PASTA-AID narrowed the expertise gap, enabling less-experienced radiologists to approach expert-level performance. Together, this work establishes an end-to-end, synthetic data-driven pipeline spanning data generation, model development, and clinical validation, thereby demonstrating substantial potential for pan-tumor research and clinical translation.
Raziehsadat Ghalamkarian, Marziehsadat Ghalamkarian, MortezaAli Ahmadi
et al.
Prostate cancer (Pca) continues to be a leading cause of cancer-related mortality in men, and the limitations in precision of traditional diagnostic methods such as the Digital Rectal Exam (DRE), Prostate-Specific Antigen (PSA) testing, and biopsies underscore the critical importance of accurate staging detection in enhancing treatment outcomes and improving patient prognosis. This study leverages machine learning and deep learning approaches, along with feature selection and extraction methods, to enhance PCa pathological staging predictions using RNA sequencing data from The Cancer Genome Atlas (TCGA). Gene expression profiles from 486 tumors were analyzed using advanced algorithms, including Random Forest (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The performance of the study is measured with respect to the F1-score, as well as precision and recall, all of which are calculated as weighted averages. The results reveal that the highest test F1-score, approximately 83%, was achieved by the Random Forest algorithm, followed by Logistic Regression at 80%, while both Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM) scored around 79%. Furthermore, deep learning models with data augmentation achieved an accuracy of 71. 23%, while PCA-based dimensionality reduction reached an accuracy of 69.86%. This research highlights the potential of AI-driven approaches in clinical oncology, paving the way for more reliable diagnostic tools that can ultimately improve patient outcomes.
Aryan Chaudhari, Ankush Singh, Sanchi Gajbhiye
et al.
In this paper we discuss lung cancer detection using hybrid model of Convolutional-Neural-Networks (CNNs) and Support-Vector-Machines-(SVMs) in order to gain early detection of tumors, benign or malignant. The work uses this hybrid model by training upon the Computed Tomography scans (CT scans) as dataset. Using deep learning for detecting lung cancer early is a cutting-edge method.
W. Shalata, Zoé Gabrielle Attal, Adam Solomon
et al.
Melanoma, a malignant neoplasm originating from melanocytes, stands as one of the most prevalent cancers globally, ranking fifth in terms of estimated new cases in recent years. Its aggressive nature and propensity for metastasis pose significant challenges in oncology. Recent advancements have led to a notable shift towards targeted therapies, driven by a deeper understanding of cutaneous tumor pathogenesis. Immunotherapy and tyrosine kinase inhibitors have emerged as promising strategies, demonstrating the potential to improve clinical outcomes across all disease stages, including neoadjuvant, adjuvant, and metastatic settings. Notably, there has been a groundbreaking development in the treatment of brain metastasis, historically associated with poor prognosis in oncology but showcasing impressive results in melanoma patients. This review article provides a comprehensive synthesis of the most recent knowledge on staging and prognostic factors while highlighting emerging therapeutic modalities, with a particular focus on neoadjuvant and adjuvant strategies, notably immunotherapy and targeted therapies, including the ongoing trials.
Background. Gastric cancer (GC) is the most common malignant tumor and ranks third in the world. LncRNA H19 (H19), one of the members of lncRNA, is overexpressed in various tumors. However, many undetermined molecular mechanisms by which H19 promotes GC progression still need to be further investigated. Methodology. A series of experiments was used to confirm the undetermined molecular mechanism including wound healing and transwell assays. Key Results. In this study, a significant upregulation of H19 expression was detected in GC cells and tissues. The poor overall survival was observed in GC patient with high H19 expression. Overexpression of H19 promoted the migration of GC cells, while knockdown of H19 significantly inhibited cell migration. Moreover, miR-148a-3p had a certain negative correlation with H19. Luciferase reporter assay confirmed that H19 could directly bind to miR-148a-3p. As expected, miR-148a mimics inhibited cell migration and invasion induced by H19 overexpression. The above findings proved that H19 functions as a miRNA sponge and verified that miR-148a-3p is the H19-associated miRNA in GC. We also confirmed that SOX-12 expression was upregulated in GC patient’s samples. SOX-12 expression was positively correlated with expression of H19 and was able to directly bind to miR-148a-3p. Importantly, in vitro wound healing assay showed that knockout of SOX-12 could reverse the promoting effect of H19 overexpression on cell migration. Conclusion. In conclusion, H19 has certain application value in the diagnosis and prognosis of GC. Specifically, H19 accelerates GCs to migration and metastasis by miR-138a-3p/SOX-12 axis.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Cytology
BackgroundMyoepithelial carcinoma (MECA) is a malignant tumor primarily affecting the salivary gland, most frequently in the parotid gland. It can manifest as primary or secondary to pleomorphic adenoma or benign myoepithelioma. MECA exhibits aggressive behaviors. In particular, primary MECA is more aggressive, frequently recurring or metastasizing distantly. Its morphological and immunohistochemical characteristics overlap with various tumors, posing challenges in its recognization as a distinct entity. Consequently, MECA may be frequently misdiagnosed, mainly when occurred in the mammary gland. This chance for misdiagnosis poses significant challenges in clinical diagnosis and treatment.Case demonstrationA 77-year-old woman with a history of pleomorphic adenoma presented with a palpable lump in the right breast for 3 months. Subsequent core needle biopsy (CNB) and modified radical mastectomy were performed, with samples subjected to histopathological examination. Based on the patient’s history, histomorphologic features, immunohistochemistry (IHC) results and results of FISH, the pathological diagnosis confirmed MECA in the mammary gland. Postoperative chemotherapy was administered, and the patient exhibited a favorable prognosis during a 40-month follow-up period.ConclusionsPrimary MECA in the mammary gland is exceedingly rare, metastasis from the salivary gland MECA to the mammary gland is even rarer and has not been previously reported. This study presents the first documented case of MECA originating from the parotid gland metastasizing to the mammary gland (also known as breast). Highlighting this case aims to raise awareness among clinical pathologists to prevent underdiagnosis and misdiagnosis of this tumor entity.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Abstract Background The proportional trends of HPV-associated oropharyngeal squamous cell carcinoma (OPSCC) according to various factors have not been analyzed in detail in previous studies. We aimed to evaluate the trends of HPV-associated OPSCC in the United States. Methods This retrospective cohort study included 13,081 patients with OPSCC from large population-based data using Surveillance, Epidemiology, and End Results (SEER) 2010–2017 database, 17 Registries. Patients were diagnosed with OPSCC primarily in the base of tongue (BOT), posterior pharyngeal wall (PPW), soft palate (SP), and tonsil and were tested for HPV infection status. We analyzed how the proportional trends of patients with OPSCC changed according to various demographic factors. Additionally, we forecasted and confirmed the trend of HPV (+) and (−) patients with OPSCC using the autoregressive integrated moving average (ARIMA) model. Results The proportion of patients who performed the HPV testing increased every year, and it has exceeded 50% since 2014 (21.95% and 51.37% at 2010 and 2014, respectively). The HPV-positive rates tended to increase over past 7 years (66.37% and 79.32% at 2010 and 2016, respectively). Positivity rates of HPV were significantly higher in OPSCC located in the tonsil or BOT than in those located in PPW or SP. The ARIMA (2,1,0) and (0,1,0) models were applied to forecast HPV (+) and (−) patients with OPSCC, respectively, and the predicted data generally matched the actual data well. Conclusion This large population-based study suggests that the proportional trends of HPV (+) patients with OPSCC has increased and will continue to increase. However, the trends of HPV (+) and (−) patients differed greatly according to various demographic factors. These results present a direction for establishing appropriate preventive measures to deal with HPV-related OPSCC in more detail.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Infectious and parasitic diseases
Benjamin Lin, Abigail K. Shelton, Kasey R. Skinner
et al.
Glioblastoma (GBM) is the most common primary malignant brain tumor with an abysmal 15-month median survival. Therefore, novel therapeutic interventions are urgently needed. EGFR is a receptor tyrosine kinase that is mutated in over 50% of GBM and is a logical target for precision oncology approaches. While aberrant EGFR signaling has been successfully targeted in other cancers, early attempts to target EGFR in GBM clinical trials have not been successful. Since these early trials, several studies have revealed that GBM EGFR biology is unique and cannot be generalized from other EGFR-driven neoplasms. To better understand EGFR biology in a GBM-specific context, we have characterized the GBM transcriptome with RNAseq, the epigenome with CUT&RUN, and the kinase proteome with Multiplexed inhibitor beads with Mass Spectrometry (MIB-MS), collectively referred to as ‘multiomics’, after modeling EGFR resistance. An isogenic mouse astrocyte (mAc) model of GBM was genetically engineered to overexpress EGFRvIII (CEv3), the most common EGFR mutation in GBM. Expression of EGFRvIII induces a unique multiomic profile that mediates many hallmark cancer phenotypes including proliferation and stemness. Chronic EGFR resistance was modeled both in vitro and in vivo through continuous exposure to erlotinib or gefitinib, EGFR tyrosine kinase inhibitors (TKI). Additionally, acute EGFR resistance was modeled using a single exposure to EGFR TKI afatinib or neratinib in vitro over a 48-hour time course. Preliminary multiomic characterization of these data has revealed several targets that can be further investigated for therapeutic exploitation. Further investigation into these targets using orthotopic allografts with CEv3 cells shows that combinatorial therapy with neratinib and abemacilib, a CDK inhibitor, significantly (p < 0.001, n= 20 mice per group) extends survival (56 days) compared to neratinib alone (31.5 days). Future integrated multiomic analysis aims to elucidate the synergistic relationship between neratinib and abemacilib.
The rapidly evolving field of digital oncopathology faces significant challenges, including the need to address diverse and complex clinical questions, often involving rare conditions, with limited availability of labeled data. These limitations hinder the development of robust AI-driven tools in the biomedical space, where accuracy in probabilistic determinations is of utmost importance. To address this, digital pathology foundation models have begun to emerge, typically developed with the size and diversity of the pre-training dataset and model parameters in mind. Here, we present CanvOI, a ViT-g/10-based foundation model designed to enhance the capabilities of digital pathology by addressing these challenges through a different approach. Considering the unique nature of oncologic histopathological images and the requirements from the embeddings to provide meaningful representations for Multiple Instance Learning (MIL) downstream models, we chose to modify the input image characteristics. By introducing larger tile sizes (380 x 380 pixels) and smaller patch sizes (10 x 10 pixels), we were able to optimize the model's performance, pushing computational resources in a new direction and achieving state-of-the-art performance on cancer-related benchmarks. CanvOI demonstrated a 1.5-7.4% improvement in averaged AUC compared to other leading foundation models built for digital pathology. Moreover, our results demonstrate that CanvOI significantly outperformed the other models, with the performance gap widening substantially when trained on just 10% of the initial cohort. This work highlights an alternative approach that, if integrated with traditional development approaches, has the potential to advance Oncology Intelligence (OI), overcome some of the current barriers and ultimately improve the clinical outcome of cancer patients.
In this manuscript, we draw on the insights from Kahneman, Sibony, and Sunsteins influential nonfiction book Noise: A Flaw in Human Judgment to explore the concept of unwanted variability in judgment (i.e., noise). We introduce key terms and connect these insights to the field of radiation oncology by illustrating how noise contributes to errors in clinically relevant areas such as contouring. Additionally, we propose practical strategies to reduce noise in radiation oncology, such as through judgment aggregation and the use of artificial intelligence tools, building on the principles outlined in the book.