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Background Lynch Syndrome (LS) is an inherited cancer predisposition syndrome defined by pathogenic variants in the mismatch repair (MMR) or EPCAM genes. In the United Kingdom, people with LS are advised to undergo biennial colonoscopy from as early as 25 until 75 years of age to mitigate a high lifetime colorectal cancer (CRC) risk, though the consideration of additional surveillance intervention(s) through the application of non-invasive diagnostic devices has yet to be longitudinally observed in LS patients. In this study, we will examine the role of annual faecal immunochemical testing (FIT) alongside biennial colonoscopy for CRC surveillance in people with LS. Methods/design In this single-arm, prospective, non-randomised study, 400 LS patients will be recruited across 11 National Health Service (NHS) Trusts throughout the United Kingdom. Study inclusion requires a LS diagnosis, between 25 and 73 years old, and a routine surveillance colonoscopy scheduled during the recruitment period. Eligible patients will receive a baseline OC-Sensor™ FIT kit ahead of their colonoscopy, and annually for 3 years thereafter. A pre-paid envelope addressed to the central lab will be included within all patient mailings for the return of FIT kits and relevant study documents. A questionnaire assessing attitudes and perception of FIT will also be included at baseline. All study samples received by the central lab will be assayed on an OC-Sensor™ PLEDIA Analyser. Patients with FIT results of ≥6 μg of Haemoglobin per gram of faeces (f-Hb) at Years 1 and/or 3 will be referred for colonoscopy via an urgent colonoscopy triage pathway. 16S rRNA gene V4 amplicon sequencing will be carried out on residual faecal DNA of eligible archived FIT samples to characterise the faecal microbiome. Discussion FIT may have clinical utility alongside colonoscopic surveillance in people with LS. We have designed a longitudinal study to examine the efficacy of FIT as a non-invasive modality. Potential limitations of this method will be assessed, including false negative or false positive FIT results related to specific morphological features of LS neoplasia or the presence of post-resection anastomotic inflammation. The potential for additional colonoscopies in a subset of participants may also impact on colonoscopic resources and patient acceptability. Trial registration Trial Registration: ISRCTN, ISRCTN15740250 . Registered 13 July 2021.

Annual lung cancer screening (LCS) can decrease lung cancer-related mortality by finding cancer at earlier, more treatable stages, yet uptake remains abysmally low in the United States, especially among adults who seldom interact with the health system. Many eligible individuals are unaware that LCS exists, underscoring the critical need for scalable, population-level communication strategies that increase awareness and engagement. The aim of this study was to evaluate reach, as defined by the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, as the extent to which the target population comes in contact with a social media-based strategy, Facebook-targeted advertisement (FBTA), designed to connect LCS-eligible individuals in the United States with a digital health communication message. The advertisement served as a digital outreach strategy for promoting engagement with LungTalk, an evidence-based intervention aimed at increasing awareness and informed decision-making about LCS. As part of the INSPIRE-Lung Study (INnovating Social Media for Prevention: LUNG Cancer Screening Awareness, Knowledge, and Uptake), 5 FBTA campaigns were launched over a 79-day period throughout the United States. Advertisements targeted adults aged 50-80 years with interests related to smoking or smoking cessation and linked to a study website where participants could complete an eligibility screener and learn more about the trial. Facebook analytics were used to assess reach, defined by the number, proportion, and demographic characteristics of individuals exposed to and interacting with FBTA content. Key metrics included total reach, impressions, link clicks, and cost-efficiency. The FBTA campaigns reached 1,048,191 unique users and generated 3,109,482 impressions (total advertisement displays, including repeat exposures to the same user). A total of 24,816 individuals clicked on the advertisements (2.37% click-through rate), and 7117 completed the eligibility screener. Of those eligible, 1272 (17.9%) met lung screening criteria, and of these, 483 (38% participation rate) enrolled in the trial. The cost per click was US $0.40, and the cost per enrolled participant was US $19.46. Individuals reached via FBTA were demographically diverse and included many who may be disconnected from traditional health care systems. FBTA is a scalable, cost-effective strategy to achieve population-level reach of LCS-eligible adults. By conceptualizing reach as exposure to an upstream digital message rather than enrollment alone, this study illustrates how social media can broaden population access to evidence-based cancer prevention tools such as LungTalk. Future research should explore embedding intervention content directly into social media platforms and tracking downstream clinical outcomes.

Mental health is becoming increasingly recognized as an important part of overall health, especially for patients with cancer. However, the relationship between nonmelanoma skin cancer and mental health has not been widely studied. The aim of this study was to examine the association between nonmelanoma skin cancer diagnosis and 2 key mental health outcomes (ie, clinical depression and the number of poor mental health days). This study used the 2023 Behavioral Risk Factor Surveillance System, a nationally representative survey of adults in the United States, which included 312,317 participants. Nonmelanoma skin cancer diagnosis, depression, and self-reported mental health days were analyzed. Logistic regression was used to evaluate the association between nonmelanoma skin cancer and depression, whereas Poisson regression was used to model the number of poor mental health days, adjusting for age, sex, race and ethnicity, education, BMI, income, and major comorbid conditions (other cancers, heart disease, lung disease, and kidney disease). Individuals with nonmelanoma skin cancer (5086/26,552, 19.15%) reported a lower overall rate of depression compared to those without nonmelanoma skin cancer (61,438/285,765, 21.50%; P<.001) but reported more poor mental health days on average (4.54, SD 8.37 d vs 3.20, SD 7.37 d; P<.001). After adjustment, nonmelanoma skin cancer diagnosis was not significantly associated with depression (adjusted odds ratio 1.01, 95% CI 0.98-1.05) and was associated with a slightly lower number of poor mental health days (adjusted rate ratio 0.94, 95% CI 0.91-0.97). Adults with nonmelanoma skin cancer experienced a meaningful mental health burden, and unadjusted analyses suggested greater day-to-day distress than among adults without nonmelanoma skin cancer. However, these differences were reduced and no longer significant for depression after adjusting for sociodemographic factors and comorbid chronic illnesses. These findings support the need for mental health screenings and support services in dermatologic and oncologic care.

Cancer remains one of the foremost global causes of mortality, with nearly 10 million deaths recorded by 2020. As incidence rates rise, there is a growing interest in leveraging machine learning (ML) to enhance prediction, diagnosis, and treatment strategies. Despite these advancements, insufficient attention has been directed toward the integration of sociodemographic variables, which are crucial determinants of health equity, into ML models in oncology. This review aims to investigate how ML techniques have been used to identify patterns of predictive association between sociodemographic factors and cancer-related outcomes. Specifically, it seeks to map current research endeavors by detailing the types of algorithms used, the sociodemographic variables examined, and the validation methodologies used. We conducted a systematic literature review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Searches were executed across 6 databases, focusing on the primary studies using ML to investigate the association between sociodemographic characteristics and cancer-related outcomes. The search strategy was informed by the PICO (population, intervention, comparison, and outcome) framework, and a set of predefined inclusion criteria was used to screen the studies. The methodological quality of each included paper was assessed. Out of the 328 records examined, 19 satisfied the inclusion criteria. The majority of studies used supervised ML techniques, with random forest and extreme gradient boosting being the most commonly used. Frequently analyzed variables include age, male or female or intersex, education level, income, and geographic location. Cross-validation is the predominant method for evaluating model performance. Nevertheless, the integration of clinical and sociodemographic data is limited, and efforts toward external validation are infrequent. ML holds significant potential for discerning patterns associated with the social determinants of cancer. Nevertheless, research in this domain remains fragmented and inconsistent. Future investigations should prioritize the integration of contextual factors, enhance model transparency, and bolster external validation. These measures are crucial for the development of more equitable, generalizable, and actionable ML applications in cancer care.

In South Korea, the cancer incidence rate has increased by 56.5% from 2001 to 2021. Nevertheless, the 5-year cancer survival rate from 2017 to 2021 increased by 17.9% compared with that from 2001 to 2005. Cancer survival rates tend to decline with lower socioeconomic status, and variations exist in the survival rates among different cancer types. Analyzing socioeconomic patterns in the survival of patients with cancer can help identify high-risk groups and ensure that they benefit from interventions. The aim of this study was to analyze differences in survival rates among patients diagnosed with six types of cancer-stomach, colorectal, liver, breast, cervical, and lung cancers-based on socioeconomic status using Korean nationwide data. This study used the Korea Central Cancer Registry database linked to the National Health Information Database to follow up with patients diagnosed with cancer between 2014 and 2018 until December 31, 2021. Kaplan-Meier curves stratified by income status were generated, and log-rank tests were conducted for each cancer type to assess statistical significance. Hazard ratios with 95% CIs for any cause of overall survival were calculated using Cox proportional hazards regression models with the time since diagnosis. The survival rates for the six different types of cancer were as follows: stomach cancer, 69.6% (96,404/138,462); colorectal cancer, 66.6% (83,406/125,156); liver cancer, 33.7% (23,860/70,712); lung cancer, 30.4% (33,203/109,116); breast cancer, 91.5% (90,730/99,159); and cervical cancer, 78% (12,930/16,580). When comparing the medical aid group to the highest income group, the hazard ratios were 1.72 (95% CI 1.66-1.79) for stomach cancer, 1.60 (95% CI 1.54-1.56) for colorectal cancer, 1.51 (95% CI 1.45-1.56) for liver cancer, 1.56 (95% CI 1.51-1.59) for lung cancer, 2.19 (95% CI 2.01-2.38) for breast cancer, and 1.65 (95% CI 1.46-1.87) for cervical cancer. A higher deprivation index and advanced diagnostic stage were associated with an increased risk of mortality. Socioeconomic status significantly mediates disparities in cancer survival in several cancer types. This effect is particularly pronounced in less fatal cancers such as breast cancer. Therefore, considering the type of cancer and socioeconomic factors, social and medical interventions such as early cancer detection and appropriate treatment are necessary for vulnerable populations.

Complementary therapies are being increasingly used by cancer survivors. As a channel for customers to share their feelings, outcomes, and perceived knowledge about the products purchased from e-commerce platforms, Amazon consumer reviews are a valuable real-world data source for understanding cancer survivorship care needs. In this study, we aimed to highlight the potential of using Amazon consumer reviews as a novel source for identifying cancer survivorship care needs, particularly related to symptom self-management. Specifically, we present a publicly available, manually annotated corpus derived from Amazon reviews of health-related products and develop baseline natural language processing models using deep learning and large language model (LLM) to demonstrate the usability of this dataset. We preprocessed the Amazon review dataset to identify sentences with cancer mentions through a rule-based method and conducted content analysis including text feature analysis, sentiment analysis, topic modeling, cancer type, and symptom association analysis. We then designed an annotation guideline, targeting survivorship-relevant constructs. A total of 159 reviews were annotated, and baseline models were developed based on deep learning and large language model (LLM) for named entity recognition and text classification tasks. A total of 4703 sentences containing positive cancer mentions were identified, drawn from 3349 reviews associated with 2589 distinct products. The identified topics through topic modeling revealed meaningful insights into cancer symptom management and survivorship experiences. Examples included discussions of green tea use during chemotherapy, cancer prevention strategies, and product recommendations for breast cancer. Top 15 symptoms in reviews were also identified, with pain being the most frequent symptom, followed by inflammation, fatigue, etc. The annotation labels were designed to capture cancer types, indicated symptoms, and symptom management outcomes. The resulting annotation corpus contains 2067 labels from 159 Amazon reviews. It is publicly accessible, together with the annotation guideline through the Open Health Natural Language Processing (OHNLP) GitHub. Our baseline model, Bert-base-cased, achieved the highest weighted average F1-score, that is, 66.92%, for named entity recognition, and LLM gpt4-1106-preview-chat achieved the highest F1-score for text classification tasks, that is, 66.67% for \"Harmful outcome,\" 88.46% for \"Favorable outcome\" and 73.33% for \"Ambiguous outcome.\" Our results demonstrate the potential of Amazon consumer reviews as a novel data source for identifying persistent symptoms, concerns, and self-management strategies among cancer survivors. This corpus, along with the baseline natural language processing models developed for named entity recognition and text classification, lays the groundwork for future methodological advancements in cancer survivorship research. Importantly, insights from this study could be evaluated against established clinical guidelines for symptom management in cancer survivorship care. By revealing the feasibility of using consumer-generated data for mining survivorship-related experiences, this study offers a promising foundation for future research and argumentation analysis aimed at improving long-term outcomes and support for cancer survivors.

Tobacco use has been identified as a risk factor for oral cancer worldwide. However, relative oral cancer incidence among adults who smoke cigarettes, use smokeless tobacco products (ST), have transitioned from cigarettes to ST, quit cigarettes and/or ST (\"quitters\"), or never used tobacco has not been well studied. We aim to present population-based oral cancer incidence rates for adults who smoke cigarettes, use ST, are former smokers who now use ST, or quit. We estimated cross-sectional incidence rates and incidence rate ratios (IRRs) using data from statewide cancer registries (Colorado, Florida, North Carolina, and Texas) and population counts derived from national surveys using combined data from 2014-2017. A random-effect meta-analysis approach was used to summarize estimates among these groups, based on multiple imputation-based IRR estimates by state and age group while considering potential heterogeneity. A total of 19,536 oral cancer cases were identified among adult males 35 years and older in the study geographies and period. The oral cancer incidence rate among adults who smoke was significantly higher than the ST group (2.6 times higher, 95% CI 2.0-3.3, P<.001), 3.6 (95% CI 3.2-4.1, P<.001) times higher than the never users, and 2.4 (95% CI 1.8-3.1, P<.001) times higher compared to former smokers who now use ST. The IRR among the ST group relative to never users was 1.4 (95% CI 1.1-1.9, P=.02). The IRR between former smokers who now use ST and those who quit was 1.4 (95% CI 1.0-2.1, P=.08). Findings from this population-based study with a large number of oral cancer cases support significantly high oral cancer incidence among adults who smoke and a lower risk of oral cancer incidence among never users, quitters, users of ST, and former smokers who now use ST compared to cigarettes. Future studies with detailed control of tobacco history and other relevant confounders are needed to confirm these findings.

BackgroundSecond cancers in patients with esophageal cancer (EC) are common and have a poor prognosis. We evaluated the incidence of second cancers at different sites by patients’ ages when their index ECs were diagnosed.MethodsThis study included patients who underwent endoscopic resection for superficial EC at our hospital between September 1994 and September 2011. Patients’ data, including sex, age at diagnosis, sequence of cancer incidence, cancer histology, and cancer site, were extracted from the cancer registry.ResultsOf 544 patients, 255 developed second cancers. Simultaneous head and neck cancers (HNCs) and other organ cancers (OCs) were, respectively, present in 15% (80/544) and 9.6% (52/544) of patients; and 30% (162/544) developed metachronous second cancers over a median follow-up period of 79.5 months (range 2–120), including 44 metachronous HNCs and 70 OCs. The cumulative incidence of metachronous HNCs was significantly higher in younger patients (< 60 years) than in older patients (≥ 60 years; P = 0.001), whereas the cumulative incidence of OCs was significantly higher in older patients than in younger patients (P = 0.03).ConclusionsThe incidence of second HNC after index EC was higher in younger-onset patients than in older-onset patients. We suggest that younger patients with EC should be carefully monitored for early detection of second HNC.

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.

Several guidelines recommend posttreatment surveillance for non-small cell lung cancer (NSCLC). However, studies evaluating surveillance patterns often cannot distinguish between imaging ordered for surveillance versus for symptoms suggestive of recurrence. Moreover, early recurrences and other competing events hamper efforts to determine true surveillance rates because of wide variability in reported guideline adherence in clinical practice. Leveraging comprehensive Veterans Health Administration data, we developed a novel competing risks framework to describe the patterns and predictors of NSCLC imaging surveillance. This study aims to examine posttreatment surveillance to estimate the true surveillance rates and predictors of guideline-concordant care in patients with early-stage NSCLC. The study cohort comprised veterans who were treated for stage 1 to 3 NSCLC between 2008 and 2016 and who survived for ≥6 months. Clinical documents and radiology reports were abstracted for image indication and clinical information. We estimated the cumulative probability of receiving guideline-concordant surveillance, defined as chest computed tomography imaging within 4 to 9 months after treatment, accounting for competing risks and censoring. Multivariable cause-specific Cox regression was used to estimate associations between patient factors and guideline-concordant surveillance, with adjustments made for multiple comparisons. The cohort consisted of 1888 patients. The mean age of the analysis cohort was 66.4 (SD 7.9) years; 95.9% (1811/1888) of the patients were male, 71.1% (1342/1888) of the patients were White, and 43.1% (814/1888) were married. Of the 1888 patients, 57% (n=1076) presented with stage 1 disease, and the most common treatment modality was surgery alone (n=1068, 56.6%). The most common type of imaging performed during the initial 120- to 270-day window was chest computed tomography (1460/3278, 44.5%). Chest X-rays accounted for 36.3% (1190/3278) of all imaging performed, while the remaining 11.8% (386/3278) and 7.4% (242/3278) were positron emission tomography scans or other imaging modalities, respectively. Compared to the years 2008 to 2010, patients treated for NSCLC from 2014 to 2016 had a significantly higher likelihood of receiving guideline-concordant surveillance (hazard ratio 1.42, P<.001). In this unique application of a competing risks framework, the rate of guideline-concordant surveillance in this national cohort was lower than that reported in many previous studies. This finding highlights a potentially substantial gap in surveillance among eligible, asymptomatic lung cancer survivors. More strategies are needed to measure the true rate of guideline-concordant surveillance, along with education and advocacy to ensure guideline-concordant care.