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Background In the past decade, systematic and comprehensive analyses of cancer genomes have identified cancer driver genes and revealed unprecedented insight into the molecular mechanisms underlying the initiation and progression of cancer. These studies illustrate that although every cancer has a unique genetic make-up, there are only a limited number of mechanisms that shape the mutational landscapes of cancer genomes, as reflected by characteristic computationally-derived mutational signatures. Importantly, the molecular mechanisms underlying specific signatures can now be dissected and coupled to treatment strategies. Systematic characterization of mutational signatures in a cancer patient’s genome may thus be a promising new tool for molecular tumor diagnosis and classification. Results In this review, we describe the status of mutational signature analysis in cancer genomes and discuss the opportunities and relevance, as well as future challenges, for further implementation of mutational signatures in clinical tumor diagnostics and therapy guidance. Conclusions Scientific studies have illustrated the potential of mutational signature analysis in cancer research. As such, we believe that the implementation of mutational signature analysis within the diagnostic workflow will improve cancer diagnosis in the future.

Liver cancer is a global health challenge, causing a significant social-economic burden. Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer, which is highly heterogeneous in terms of molecular and cellular signatures. Early-stage or small tumors are typically treated with surgery or ablation. Currently, chemotherapies and immunotherapies are the best treatments for unresectable tumors or advanced HCC. However, drug response and acquired resistance are not predictable with the existing systematic guidelines regarding mutation patterns and molecular biomarkers, resulting in sub-optimal treatment outcomes for many patients with atypical molecular profiles. With advanced technological platforms, valuable information such as tumor genetic alterations, epigenetic data, and tumor microenvironments can be obtained from liquid biopsy. The inter- and intra-tumoral heterogeneity of HCC are illustrated, and these collective data provide solid evidence in the decision-making process of treatment regimens. This article reviews the current understanding of HCC detection methods and aims to update the development of HCC surveillance using liquid biopsy. Recent critical findings on the molecular basis, epigenetic profiles, circulating tumor cells, circulating DNAs, and omics studies are elaborated for HCC diagnosis. Besides, biomarkers related to the choice of therapeutic options are discussed. Some notable recent clinical trials working on targeted therapies are also highlighted. Insights are provided to translate the knowledge into potential biomarkers for detection and diagnosis, prognosis, treatment response, and drug resistance indicators in clinical practice.

Carbon Dots (CDs) are the latest members of carbon-based nanomaterials, which since their discovery have attracted notable attention due to their chemical and mechanical properties, brilliant fluorescence, high photostability, and good biocompatibility. Together with the ease and affordable preparation costs, these intrinsic features make CDs the most promising nanomaterials for multiple applications in the biological field, such as bioimaging, biotherapy, and gene/drug delivery. This review will illustrate the most recent applications of CDs in the biomedical field, focusing on their biocompatibility, fluorescence, low cytotoxicity, cellular uptake, and theranostic properties to highlight above all their usefulness as a promising tool for cancer diagnosis and therapy.

Background Sarcoid lesions may mimic metastatic disease or recurrence in thyroid cancer (TC) patients as both diseases may affect the lungs and lymph nodes. We present the first study to systematically evaluate the clinical course of patients with (TC) after adjuvant radioactive iodine therapy (RIT) and concomitant sarcoidosis of the lung or the lymph nodes. Methods We screened 3285 patients and retrospectively identified 16 patients with TC (11 papillary thyroid cancer (PTC), 3 follicular thyroid cancer (FTC), 1 oncocytic PTC, 1 oncocytic FTC) and coexisting sarcoidosis of the lung and/or the lymph nodes treated at our institute. All patients had undergone thyroidectomy and initial adjuvant RIT. Challenges in diagnosing and the management of these patients were evaluated during long term follow-up (median 4.9 years (0.8–15.0 years)). Results Median age at first diagnosis of TC was 50.1 years (33.0–71.5 years) and of sarcoidosis 39.4 years (18.0–63.9 years). During follow-up, physicians were able to differentiate between SA and persistent or recurrent TC in 10 of 16 patients (63%). Diagnosis was complicated by initial negative thyroglobulin (Tg), positive Tg antibodies and non-specific imaging findings. Histopathology can reliably distinguish between SA and TC in patients with one suspicious lesion. Conclusion Physicians should be aware of the rare coexistence of sarcoidosis and TC. Lymphadenopathy and pulmonary lesions could be metastases, sarcoidosis or even a mix of both. Therefore, this rare patient group should receive a thorough work up including histopathological clarification and, if necessary, separately for each lesion.

Background Racial disparities are well-documented in preventive cancer care, but they have not been fully explored in the context of lung cancer screening. We sought to explore racial differences in lung cancer screening outcomes within a lung cancer screening program (LCSP) at our urban academic medical center including differences in baseline low-dose computed tomography (LDCT) results, time to follow-up, adherence, as well as return to annual screening after additional imaging, loss to follow-up, and cancer diagnoses in patients with positive baseline scans. Methods A historical cohort study of patients referred to our LCSP was conducted to extract demographic and clinical characteristics, smoking history, and lung cancer screening outcomes. Results After referral to the LCSP, blacks had significantly lower odds of receiving LDCT compared to whites, even while controlling for individual lung cancer risk factors and neighborhood-level factors. Blacks also demonstrated a trend toward delayed follow-up, decreased adherence, and loss to follow-up across all Lung-RADS categories. Conclusions Overall, lung cancer screening annual adherence rates were low, regardless of race, highlighting the need for increased patient education and outreach. Furthermore, the disparities in race we identified encourage further research with the purpose of creating culturally competent and inclusive LCSPs.

Purpose We examined cancer screening practices and related beliefs in cancer survivors and individuals with family or close friends with a cancer diagnosis compared to individuals without the above cancer history for 5 population-based (gastric, colorectal, lung, breast, cervical) and 1 opportunistic (prostate) cancer screenings using nationally representative cross-sectional survey in Japan. Methods We analyzed 3269 data from 3605 respondents (response rate, 37.1%) and compared the screening beliefs and practices of cancer survivors ( n  = 391), individuals with family members ( n  = 1674), and close friends with a cancer diagnosis ( n  = 685) to those without any cancer history ( n  = 519). Results Being a cancer survivor was associated with screening for gastric (OR, 1.75; 95% CI, 1.04–2.95), colorectal (OR, 1.56; 95% CI, 1.03–2.36), and lung cancer (OR, 1.71; 95% CI, 1.10–2.66) but not breast, cervical cancer or PSA test. Having a family cancer diagnosis was associated with colorectal and lung cancer screening. Having friends with a cancer diagnosis was associated with PSA test. Cancer survivors and family members perceived themselves as being more susceptible and worried about getting cancer than individuals without any cancer history. Cancer survivors strongly believed screening can detect cancer and were more likely to undergo screening. Subgroup analysis indicated an interrelation between gastric and colorectal cancer screening among survivors. Conclusions A cancer diagnosis in oneself or family or friend influences an individual’s health-related belief and risk perception, which can increase the likelihood of cancer screening. Implications for Cancer Survivors Targeted and tailored communication strategies can increase awareness of cancer screening.

IntroductionLung cancer has the highest mortality rate among all cancer types globally, largely due to delayed or ineffective diagnosis and treatment. Radiomics is commonly used to diagnose lung cancer, especially in later stages or during routine screenings. However, frequent radiological imaging poses health risks, and while advanced diagnostic alternatives exist, they are often costly and accessible only to a limited, privileged population. Leveraging clinical data using machine learning (ML) and artificial intelligence (AI) enables a safer, more inclusive, and affordable solution. Due to a lack of interpretability, AI-based models for cancer diagnosis are less adopted by clinicians.MethodsThis study introduces a safe, inclusive, and cost-effective lung cancer diagnostic method using an explainable AI (XAI) model built on routine clinical data. It employs a stacking ensemble of Artificial Neural Network (ANN) and Deep Neural Network (DNN) to match the diagnostic performance of clean-data DNN models. By incorporating rare medical cases through Adaptive Synthetic Sampling (ADASYN), the model reduces the risk of missing challenging, rare-case diagnoses.ResultsThe proposed XAI model demonstrates strong performance with an accuracy of 0.8558, AUC of 0.8600, precision of 0.8092, recall of 0.9282, and F1-score of 0.8646, notably improving rare case detection by over 50%. SHapley additive exPlanations(SHAP)-based interpretability highlights Erythrocyte sedimentation rate(ESR), intoxication-related factors, hemoglobin levels, and neutrophil counts as key features. The model also reveals associations, such as a link between heavy tobacco use and elevated ESR. Counterfactual explanations help identify features contributing to misdiagnoses by exposing sources of confusion in the model's decisions.ConclusionGiven the limited dataset size and geographic constraints, this research should be viewed as a prototype and in its current form, the model is best suited as a pre-screening tool to support early detection. With training on larger and more diverse datasets, the model has strong potential to evolve into a robust and scalable diagnostic solution.

Keywords: Cell-free DNA, Early cancer diagnosis, DNA fragmentation patterns, Artificial intelligence, Cancer screening

In a recent study, we have demonstrated that real-time Raman spectroscopy could be used for skin cancer diagnosis. As a translational study, the objective of this study is to validate previous findings through a completely independent clinical test. In total, 645 confirmed cases were included in the analysis, including a cohort of 518 cases from a previous study, and an independent cohort of 127 new cases. Multi-variant statistical data analyses including principal component with general discriminant analysis (PC-GDA) and partial least squares (PLS) were used separately for lesion classification, which generated similar results. When the previous cohort ( n  = 518) was used as training and the new cohort ( n  = 127) was used as testing, the area under the receiver operating characteristic curve (ROC AUC) was found to be 0.889 (95 % CI 0.834–0.944; PLS); when the two cohorts were combined, the ROC AUC was 0.894 (95 % CI 0.870–0.918; PLS) with the narrowest confidence intervals. Both analyses were comparable to the previous findings, where the ROC AUC was 0.896 (95 % CI 0.846–0.946; PLS). The independent study validates that real-time Raman spectroscopy could be used for automatic in vivo skin cancer diagnosis with good accuracy.

Diagnostic imaging and radionuclide therapy of prostate (PC) and breast cancer (BC) using radiolabeled gastrin-releasing peptide receptor (GRPR)-antagonists represents a promising approach. We herein propose the GRPR-antagonist based radiotracer [99mTc]Tc-DB15 ([99mTc]Tc-N4-AMA-DGA-DPhe6,Sar11,LeuNHEt13]BBN(6-13); N4: 6-carboxy-1,4,8,11-tetraazaundecane, AMA: aminomethyl-aniline, DGA: diglycolic acid) as a new diagnostic tool for GRPR-positive tumors applying SPECT/CT. The uptake of [99mTc]Tc-DB15 was tested in vitro in mammary (T-47D) and prostate cancer (PC-3) cells and in vivo in T-47D or PC-3 xenograft-bearing mice as well as in BC patients. DB15 showed high GRPR-affinity (IC50 = 0.37 ± 0.03 nM) and [99mTc]Tc-DB15 strongly bound to the cell-membrane of T-47D and PC-3 cells, according to a radiolabeled antagonist profile. In mice, the radiotracer showed high and prolonged GRPR-specific uptake in PC-3 (e.g., 25.56 ± 2.78 %IA/g vs. 0.72 ± 0.12 %IA/g in block; 4 h pi) and T-47D (e.g., 15.82 ± 3.20 %IA/g vs. 3.82 ± 0.30 %IA/g in block; 4 h pi) tumors, while rapidly clearing from background. In patients with advanced BC, the tracer could reveal several bone and soft tissue metastases on SPECT/CT. The attractive pharmacokinetic profile of [99mTc]DB15 in mice and its capability to target GRPR-positive BC lesions in patients highlight its prospects for a broader clinical use, an option currently being explored by ongoing clinical studies.