Prevalence and risk factors of thyroid nodules in breast cancer women with different clinicopathological characteristics: a cross-sectional study.

BACKGROUND: Association between breast cancer (BC) and thyroid nodules (TNs) is still unclear. This research was to estimate the prevalence and risk factors of TN in Chinese BC women at initial diagnosis. METHODS: 1731 Chinese early-stage BC women at initial diagnosis underwent thyroid ultrasound and 1:1 age-matched Chinese healthy women underwent health examination in corresponding period were enrolled for analysis. RESULTS: Prevalence of TN and TI-RADS ≥ 4 TN in BC patients (56.27% and 9.76%) were higher than healthy people (46.04% and 5.49%), respectively, P < 0.001. Among BC patients, prevalence of TN and TI-RADS ≥ 4 TN in hormone receptor (HR)-positive patients (59.57% and 11.81%) were higher than HR-negative patients (48.77% and 5.10%), respectively, P < 0.001, while without difference between HR-negative patients and healthy people. After adjusting for age and BMI, HR-positive patients had higher risk of TN (OR = 1.546, 95%CI 1.251-1.910, P < 0.001) and TI-RADS ≥ 4 TN (OR = 3.024, 95%CI 1.943-4.708, P < 0.001) than HR-negative patients. Furthermore, the risk of TI-RADS ≥ 4 TN was higher in patients with estrogen receptor (ER) positive (OR = 2.933, 95%CI 1.902-4.524), progesterone receptor (PR) positive (OR = 1.973, 95%CI 1.378-2.826), Ki-67 < 20% (OR = 1.797, 95%CI 1.280-2.522), and tumor size < 2 cm (OR = 1.804, 95%CI 1.276-2.552), respectively, P < 0.001. CONCLUSIONS: Prevalence of TN, especially TI-RADS ≥ 4 TN, in Chinese early-stage BC women was higher than healthy people. HR-positive patients had higher prevalence and risk of TN, while without difference between HR-negative patients and healthy people. The increased risk of TN was correlated with ER-positive, PR-positive, lower Ki-67 expression, and smaller tumor size.

Application of the 'online assessment + FOBT at home' to improve participation and the efficacy of opportunistic screening for colorectal cancer: a retrospective cohort study.

BACKGROUND: Colorectal cancer (CRC) screening faces two major challenges: insufficient screening coverage and poor adherence. A smartphone applet named "Early Screening Assistant (ESA)" was developed to create an online risk-assessment and fecal occult blood test (FOBT) at home. This retrospective study was designed to evaluate whether the new CRC screening strategy can improve the colonoscopy participation rate (PR) and lesion detection rate (DR). METHODS: In total, 6194 individuals who accepted normal health examinations and CRC screening based on the ESA from June 2020 to May 2022 were assigned to the ESA group. Accordingly, 7923 inhabitants who only accepted normal health examinations were assigned to the control group. The colonoscopy PR and neoplastic lesion DR were then compared between the two groups. RESULTS: Overall, a higher proportion of subjects in the ESA group (285 of 6194 [4.6%]) completed colonoscopy than in the control group (126 of 7923, [1.6%]), p < 0.01). The neoplastic lesion DR also significantly increased in the ESA group (76 of 6194 [1.22%]) compared with the control group (15 of 7923 [0.19%]) (p < 0.01). The adjusted diagnostic sensitivity and specificity of the "Online assessment + FOBT at home" were 41.5% and 62.6% for neoplastic lesions, respectively. CONCLUSIONS: This retrospective cohort study confirmed that the new CRC screening strategy based on the "Online assessment + FOBT at home" can improve colonoscopy participation and the neoplastic lesion detection rate and may represent a promising screening strategy for CRC. TRIAL REGISTRATION: This study was registered in China Clinical Trial Registry ( https://www.chictr.org.cn ) on 29/09/2022. REGISTRATION NUMBER: ChiCTR2200064186.

Long short-term memory model - A deep learning approach for medical data with irregularity in cancer predication with tumor markers.

BACKGROUND: Machine learning (ML) has emerged as a superior method for the analysis of large datasets. Application of ML is often hindered by incompleteness of the data which is particularly evident when approaching disease screening data due to varied testing regimens across medical institutions. Here we explored the utility of multiple ML algorithms to predict cancer risk when trained using a large but incomplete real-world dataset of tumor marker (TM) values. METHODS: TM screening data were collected from a large asymptomatic cohort (n = 163,174) at two independent medical centers. The cohort included 785 individuals who were subsequently diagnosed with cancer. Data included levels of up to eight TMs, but for most subjects, only a subset of the biomarkers were tested. In some instances, TM values were available at multiple time points, but intervals between tests varied widely. The data were used to train and test various machine learning models to evaluate their robustness for predicting cancer risk. Multiple methods for data imputation were explored and models were developed for both single time-point as well as time-series data. RESULTS: The ML algorithm, long short-term memory (LSTM), demonstrated superiority over other models for dealing with irregular medical data. A cancer risk prediction tool was trained and validated for a single time-point test of a TM panel including up to four biomarkers (AUROC = 0.831, 95% CI: 0.827-0.835) which outperformed a single threshold method using the same biomarkers. A second model relying on time series data of up to four time-points for 5 TMs had an AUROC of 0.931. CONCLUSIONS: A cancer risk prediction tool was developed by training a LSTM model using a large but incomplete real-world dataset of TM values. The LSTM model was best able to handle irregular data compared to other ML models. The use of time-series TM data can further improve the predictive performance of LSTM models even when the intervals between tests vary widely. These risk prediction tools are useful to direct subjects to further screening sooner, resulting in earlier detection of occult tumors.

The combination of methylenehydrofolate reductase C677T polymorphism screening and gastrointestinal tumor markers detection may be an early screening method for gastrointestinal cancer related to helicobacter pylori infection.

Methyltetrahydrofolate reductase (MTHFR) is a key enzyme in folate metabolism, and its single nucleotide polymorphism (SNP) site C677T may be associated with gastrointestinal cancer. However, the relationship between MTHFR C677T polymorphism and gastrointestinal tumor markers carcinoma embryonic antigen (CEA), carbohydrate antigen 199 (CA199) and carbohydrate antigen 724 (CA724) in Helicobacter pylori (H. pylori) infection is not specified. This study aims to identify the association between MTHFR C677T polymorphism and gastrointestinal tumor markers (CEA, CA199 and CA724) in H. pylori infection. The relationship between MTHFR C677T polymorphism and gastrointestinal tumor markers in 58 patients with H. pylori infection and 94 non-infected patients was studied. We found that TT genotype was a susceptibility factor of H. pylori infection, which was also associated with increased CEA and CA724 levels. Moreover, there was a negative additive interaction between MTHFR gene C677T polymorphism and CEA levels in H.pylori infection. Meanwhile, there were significant differences in CEA levels between MTHFR C677T polymorphism and H.pylori infection. The presence of T allele led to a decrease in CEA levels when 13C urea breath test (13C-UBT) was positive, while the presence of T allele led to an increase in CEA levels when 13C-UBT was negative. Therefore, we suggest that healthy people should take MTHFR C677T polymorphism screening, combined with 13C-UBT and gastrointestinal tumor markers detection, which can screen out the susceptible population of H. pylori, and help to detect gastrointestinal cancer in the early stage.

Prevalence and associated metabolic factors for thyroid nodules: a cross-sectional study in Southwest of China with more than 120 thousand populations.

OBJECTIVE: To explore the prevalence and its associated metabolic factors of thyroid nodules (TNs) among subjects who participated in the physical examinations in Chongqing, China. METHODS: The participants from the Health Management Center of JinShan Hospital of Chongqing Medical University, between September 2015 and May 2020, were included in this study. All of the participants underwent thyroid ultrasonography, anthropometric measurements, and serum examinations. Differences in the TNs prevalence were compared with the chi-square test or Wilcoxon rang-sum test. Multivariable logistic regression analyses were used to estimate the metabolic factors associated with TNs and multiple thyroid nodules (MTNs). RESULTS: Of the included 121,702 participants, 41,547 had TNs, and 20,899 had MTNs, with the prevalence of 34.1 and 17.0 %, respectively. Women had a significantly higher prevalence of TNs than men (40.6 % vs. 29.8 %; χ2 = 1517.33, P < 0.001), and TNs prevalence was gradually increased with age (P for trend < 0.001). Female gender, advanced age, and metabolic factors including central obesity, hypertension, diabetes and fatty liver were positively associated with TNs; BMI, hyperlipoidemia and hyperuricemia were not independent risk factors of TNs. While female gender, advanced age, central obesity, hypertension and diabetes were independent risk factors of MTNs. CONCLUSIONS: The prevalence of thyroid nodules was relatively high. The associated factors identified in this study could help the clinicians to detect the high-risk patients and make targeted screening strategies for the preventing of the occurrence of TNs.