Cancer Risk Prediction Research Articles

Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort

To identify the risk factors related to lifestyle behaviors that affect the incidence of lung cancer, to build a lung cancer risk prediction model to identify, in the population, individuals who are at high risk, and to facilitate the early detection of lung cancer. The data used in the study were obtained from the UK Biobank, a database that contains information collected from 502 389 participants between March 2006 and October 2010. Based on domestic and international guidelines for lung cancer screening and high-quality research literature on lung cancer risk factors, high-risk population identification criteria were determined. Univariate Cox regression was performed to screen for risk factors of lung cancer and a multifactor lung cancer risk prediction model was constructed using Cox proportional hazards regression. Based on the comparison of Akaike information criterion and Schoenfeld residual test results, the optimal fitted model assuming proportional hazards was selected. The multiple factor Cox proportional hazards regression was performed to consider the survival time and the population was randomly divided into a training set and a validation set by a ratio of 7:3. The model was built using the training set and the performance of the model was internally validated using the validation set. The area under the receiver operating characteristic (ROC) curve ( AUC) was used to evaluate the efficacy of the model. The population was categorized into low-risk, moderate-risk, and high-risk groups based on the probability of occurrence of 0% to <25%, 25% to <75%, and 75% to 100%. The respective proportions of affected individuals in each risk group were calculated. The study eventually covered 453 558 individuals, and out of the cumulative follow-up of 5 505 402 person-years, a total of 2 330 cases of lung cancer were diagnosed. Cox proportional hazards regression was performed to identify 10 independent variables as predictors of lung cancer, including age, body mass index (BMI), education, income, physical activity, smoking status, alcohol consumption frequency, fresh fruit intake, family history of cancer, and tobacco exposure, and a model was established accordingly. Internal validation results showed that 8 independent variables (all the 10 independent variables screened out except for BMI and fresh fruit intake) were significant influencing factors of lung cancer ( P<0.05). The AUC of the training set for predicting lung cancer occurrence at one year, five years, and ten years were 0.825, 0.785, and 0.777, respectively. The AUC of the validation set for predicting lung cancer occurrence at one year, five years, and ten years were 0.857, 0.782, and 0.765, respectively. 68.38% of the individuals who might develop lung cancer in the future could be identified by screening the high-risk population. We established, in this study, a model for predicting lung cancer risks associated with lifestyle behaviors of a large population. Showing good performance in discriminatory ability, the model can be used as a tool for developing standardized screening strategies for lung cancer.

Risk prediction of bladder cancer among person with diabetes: A derivation and validation study.

This study aimed to devise and validate a clinical scoring system for risk prediction of bladder cancer to guide urgent cystoscopy evaluation among people with diabetes. People with diabetes who received cystoscopy from a large database in the Chinese population (2009-2018). We recruited a derivation cohort based on random sampling from 70% of all individuals. We used the adjusted odds ratios (aORs) for independent risk factors to devise a risk score, ranging from 0 to 5: 0-2 'average risk' (AR) and 3-5 'high risk' (HR). A total of 5905 people with diabetes, among whom 123 people with BCa were included. The prevalence rate in the derivation (n = 4174) and validation cohorts (n = 1731) was 2.2% and 1.8% respectively. Using the scoring system constructed, 79.6% and 20.4% in the derivation cohort were classified as AR and HR respectively. The prevalence rate in the AR and HR groups was 1.57% and 4.58% respectively. The risk score consisted of age (18-70: 0; >70: 2), male sex (1), ever/ex-smoker (1) and duration of diabetes (≥10 years: 1). Individuals in the HR group had 3.26-fold (95% CI = 1.65-6.44, p = 0.025) increased prevalence of bladder than the AR group. The concordance (c-) statistics was 0.72, implying a good discriminatory capability of the risk score to stratify high-risk individuals who should consider earlier cystoscopy. The risk prediction algorithm may inform urgency of cystoscopy appointments, thus allowing a more efficient use of resources and contributing to early detection of BCa among people planned to be referred.

Non-targeted effects and space radiation risks for astronauts on multiple International Space Station and lunar missions

Future space travel to the earth's moon or the planet Mars will likely lead to the selection of experienced International Space Station (ISS) or lunar crew persons for subsequent lunar or mars missions. Major concerns for space travel are galactic cosmic ray (GCR) risks of cancer and circulatory diseases. However large uncertainties in risk prediction occur due to the quantitative and qualitative differences in heavy ion microscopic energy deposition leading to differences in biological effects compared to low LET radiation. In addition, there are sparse radiobiology data and absence of epidemiology data for heavy ions and other high LET radiation. Non-targeted effects (NTEs) are found in radiobiology studies to increase the biological effectiveness of high LET radiation at low dose for cancer related endpoints. In this paper the most recent version of the NASA Space Cancer Risk model (NSCR-2022) is used to predict mission risks while considering NTEs in solid cancer risk predictions. I discuss predictions of space radiation risks of cancer and circulatory disease mortality for US Whites and US Asian-Pacific Islander (API) populations for 6-month ISS, 80-day lunar missions, and combined ISS-lunar mission. Model predictions suggest NTE increase cancer risks by about ∼2.3 fold over a model that ignores NTEs. US API are predicted to have a lower cancer risks of about 30% compared to US Whites. Cancer risks are slightly less than additive for multiple missions, which is due to the decease of risk with age of exposure and the increased competition with background risks as radiation risks increase. The inclusion of circulatory risks increases mortality estimates about 25% and 37% for females and males, respectively in the model ignoring NTEs, and 20% and 30% when NTEs are assumed to modify solid cancer risk. The predictions made here for combined ISS and lunar missions suggest risks are within risk limit recommendations by the National Council on Radiation Protection and Measurements (NCRP) for such missions.

Development and validation of colorectal cancer risk prediction tools: A comparison of models

BackgroundIdentification of individuals at elevated risk can improve cancer screening programmes by permitting risk-adjusted screening intensities. Previous work introduced a prognostic model using sex, age and two preceding faecal haemoglobin concentrations to predict the risk of colorectal cancer (CRC) in the next screening round. Using data of 3 screening rounds, this model attained an area under the receiver-operating-characteristic curve (AUC) of 0.78 for predicting advanced neoplasia (AN). We validated this existing logistic regression (LR) model and attempted to improve it by applying a more flexible machine-learning approach. MethodsWe trained an existing LR and a newly developed random forest (RF) model using updated data from 219,257 third-round participants of the Dutch CRC screening programme until 2018. For both models, we performed two separate out-of-sample validations using 1,137,599 third-round participants after 2018 and 192,793 fourth-round participants from 2020 onwards. We evaluated the AUC and relative risks of the predicted high-risk groups for the outcomes AN and CRC. ResultsFor third-round participants after 2018, the AUC for predicting AN was 0.77 (95% CI: 0.76–0.77) using LR and 0.77 (95% CI: 0.77–0.77) using RF. For fourth-round participants, the AUCs were 0.73 (95% CI: 0.72–0.74) and 0.73 (95% CI: 0.72–0.74) for the LR and RF models, respectively. For both models, the 5% with the highest predicted risk had a 7-fold risk of AN compared to average, whereas the lowest 80% had a risk below the population average for third-round participants. ConclusionThe LR is a valid risk prediction method in stool-based screening programmes. Although predictive performance declined marginally, the LR model still effectively predicted risk in subsequent screening rounds. An RF did not improve CRC risk prediction compared to an LR, probably due to the limited number of available explanatory variables. The LR remains the preferred prediction tool because of its interpretability.

Incidence risk assessment of secondary cancer due to radiotherapy of women with rectal cancer using BEIR VII, EPA, and ICRP models.

Radiotherapy has a significant side effect known as radiation-induced secondary cancer. This study aims to evaluate the dose and secondary cancer risk for women with rectal cancer treated with three-dimensional conformal radiation therapy (3D-CRT) to the organs at risk (OARs) and some sensitive organs using different types of radiation-induced cancer risk prediction models, including Biological Effects of Ionizing Radiation (BEIRVII), Environmental Protection Agency (EPA) and International Commission on Radiological Protection (ICRP), and compare the results of the different models for same organs. Thirty female patients with rectal cancer were considered and dose calculations were based on the PCRT-3D treatment planning system, while the radiotherapy of the patients had been performed using Shinva linear accelerator with a total dose of 45 Gy at 25 fractions. Planning target volume (PTV), OARs, and some sensitive organs were contoured, three models were used to evaluate secondary cancer risk (SCR) using the excess relative risk (ERR) and excess absolute risk (EAR). The bladder presents the highest risk, in terms of ERR, and the femur head and uterus in terms of EAR from the three models (BEIR VII, EPA, and ICRP). Based on the obtained results, radiotherapy of rectal cancer is relatively higher for the bladder and femur head, compared to the risk for other organs, the kidney risk is significantly lower. It was observed that the SCR from the ICRP model was higher compared to BEIR VII and EPA models.

A breast cancer risk predication and classification model with ensemble learning and big data fusion

Breast cancer is a major health issue for women all over the world. Effective care and better patient outcomes depend on early identification and precise risk prediction. Ensemble learning algorithms and merging large data have recently emerged as potential ways to predict and classify breast cancer risk. This paper aims to investigate the feasibility of using ensemble learning and big data fusion for risk prediction and categorization of breast cancer. The key focus is building a reliable and precise model by combining the best features of various learning algorithms and a variety of vast and varied data sets. This research presents an Improved XGBoost Ensembling (I-XGBoost) technique with big data analytics to predict and diagnose breast cancer cells. The proposed work considers three critical identification phases to achieve greater accuracy: data pre-processing, feature extraction, and target role. The Wisconsin breast cancer diagnostic data was used for testing the proposed method. It is also compared in terms of performance with other classification methods like Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, Adaboost, and XGBoost. This research aims to determine which characteristics are most beneficial in predicting cancer as malignant or benign. We show that I-XGBoost has an impressively high accuracy score of 99.84% using Spark’s Python Application Programming Interface (API). This research emphasizes the promise of ensemble learning and big data fusion for predicting and classifying breast cancer risks. By proposing a new method that draws on the best features of several models and uses the wealth of information available in big data, it adds to the current body of knowledge. Positive findings highlight the practical use of the suggested paradigm in clinical contexts.

Clinical Impact of Polygenic Risk Score for Breast Cancer Risk Prediction in 382 Individuals with Hereditary Breast and Ovarian Cancer Syndrome.

Single nucleotide polymorphisms are currently not considered in breast cancer (BC) risk predictions used in daily practice of genetic counselling and clinical management of familial BC in Germany. This study aimed to assess the clinical value of incorporating a 313-variant-based polygenic risk score (PRS) into BC risk calculations in a cohort of German women with suspected hereditary breast and ovarian cancer syndrome (HBOC). Data from 382 individuals seeking counselling for HBOC were analysed. Risk calculations were performed using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm with and without the inclusion of the PRS. Changes in risk predictions and their impact on clinical management were evaluated. The PRS led to changes in risk stratification based on 10-year risk calculations in 13.6% of individuals. Furthermore, the inclusion of the PRS in BC risk predictions resulted in clinically significant changes in 12.0% of cases, impacting the prevention recommendations established by the German Consortium for Hereditary Breast and Ovarian Cancer. These findings support the implementation of the PRS in genetic counselling for personalized BC risk assessment.

Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review

The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms between each model, but have reached controversial conclusions. Thus, the performance of current machine-learning-based gastric cancer risk prediction models alongside the clinical relevance of different predictive factors needs to be evaluated to help build more efficient and feasible models in the future. In this systematic review, we summarize the current research progress related to the gastric cancer risk prediction model; discuss the predictive factors and methods used to construct the model; analyze the role of important predictive factors in gastric cancer, the preference of the selected classification algorithm, and the emphasis of evaluation criteria; and provide suggestions for the subsequent construction and improvement of the gastric cancer risk prediction model. Finally, we propose an improved approach based on the ethical issues of artificial intelligence in medicine to realize the clinical application of the gastric cancer risk prediction model in the future.

Application of machine learning techniques in the prediction of excess lifetime cancer risks of agricultural byproducts used as building and construction materials

Application of machine learning techniques in the prediction of excess lifetime cancer risks of agricultural byproducts used as building and construction materials

Spatiotemporal Mammography-based Deep Learning Model for Improved Breast Cancer Risk Prediction.

Breast cancer is one of the most prevalent cancers among women. It is the second leading cause of death in cancer-related deaths. Early detection and personalized risk assessment can reduce the mortality rate and improve survival rates. Classical risk prediction models which rely on traditional risk factors produce inconsistent results among the different populations. Thus, they are not routinely used in screening programs. Deep learning was proven to improve the results of breast cancer risk prediction. CNNs can detect risk cues from screening mammograms. However, the deep learning models utilize the spatial information of each screening mammogram independently. This study aims to further improve the risk prediction models by exploiting the spatiotemporal information in multiple screening time points. We implemented a Siamese neural network for spatiotemporal risk prediction and compared the results against CNN trained using two different time points (T1 and T2) independently. We tested our results on 191 cases, 61 of which were diagnosed with cancer. The Siamese model showed a superior AUC of 0.81 against 0.75 and 0.77 at T1 and T2 respectively. The Siamese network also exhibited higher accuracy and F1-score with values of 0.78 and 0.61 while CNNs have the same accuracy of 0.76 with an F1-score of 0.54 at T1, and 0.59 at T2. The results suggest that spatiotemporal risk prediction can be a more reliable risk assessment tool.

Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe

BackgroundBreast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups.PurposeTo systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC.MethodsA systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models’ final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots.ResultsFrom a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models.ConclusionsGiven the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.

Development of lung cancer risk prediction models based on F-18 FDG PET images.

We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET). We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set. The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (p < 0.001 and p < 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07, p < 0.001; male: OR 2.08, p = 0.015; BMI: OR 0.93, p = 0.057; current or past smoker: OR 5.60, p < 0.001; lung SUVR: OR 1.13, p < 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06, p < 0.001; male: OR 1.87, p = 0.045; BMI: OR 0.93, p = 0.010; current or past smoker: OR 4.78, p < 0.001; lung MH index: OR 1.33, p < 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively]. The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.

A risk prediction model of cervical cancer developed based on nested case-control design

Objective: To construct a cervical cancer risk prediction model based on nested case-control study design and Yinzhou Health Information Platform in Ningbo, and provide reliable reference for self-risk assessment of cervical cancer in local women. Methods: In local women aged 25-75 years old who had no history of cervical cancer registered in Yinzhou before October 31, 2018, a follow up was conducted for at least three years, the patients who developed cervical cancer during the follow up period were selected as the case group and matched with a control group at a ratio of 1∶10. The prediction indicators before the onset was used in model construction. Variables were selected by Lasso-logistic regression, the variables with non-zero β were selected to fit the logistic regression model and Bootstrap was used for internal validation. The discrimination of the model was evaluated by area under the receiver operating characteristic curve(AUROC), and the calibration was evaluated by calibration curve and Hosmer-Lemeshow test. Results: The prediction indicators included in the final model were age, smoking status, history of cervicitis, history of adenomyosis, HPV testing, and thinprep cytologic test. The AUROC calculated in the internal validation was 0.740 (95%CI:0.739-0.740), and the calibration curve was almost identical with the ideal curve, P=0.991 in Hosmer-Lemeshow test, indicating that the model discrimination and calibration were good. Conclusions: In this study, a simple and practical cervical cancer risk prediction model was developed. The model can be used in general population with strong interpretability, good discrimination and calibration in internal validation, which can provide a reference for women to assess their risk of cervical cancer.

Lung cancer risk score for ever and never smokers in China.

Most lung cancer risk prediction models were developed in European and North-American cohorts of smokers aged ≥ 55 years, while less is known about risk profiles in Asia, especially for never smokers or individuals aged < 50 years. Hence, we aimed to develop and validate a lung cancer risk estimate tool for ever and never smokers across a wide age range. Based on the China Kadoorie Biobank cohort, we first systematically selected the predictors and explored the nonlinear association of predictors with lung cancer risk using restricted cubic splines. Then, we separately developed risk prediction models to construct a lung cancer risk score (LCRS) in 159,715 ever smokers and 336,526 never smokers. The LCRS was further validated in an independent cohort over a median follow-up of 13.6 years, consisting of 14,153 never smokers and 5,890 ever smokers. A total of 13 and 9 routinely available predictors were identified for ever and never smokers, respectively. Of these predictors, cigarettes per day and quit years showed nonlinear associations with lung cancer risk (Pnon-linear < 0.001). The curve of lung cancer incidence increased rapidly above 20 cigarettes per day and then was relatively flat until approximately 30 cigarettes per day. We also observed that lung cancer risk declined sharply within the first 5 years of quitting, and then continued to decrease but at a slower rate in the subsequent years. The 6-year area under the receiver operating curve for the ever and never smokers' models were respectively 0.778 and 0.733 in the derivation cohort, and 0.774 and 0.759 in the validation cohort. In the validation cohort, the 10-year cumulative incidence of lung cancer was 0.39% and 2.57% for ever smokers with low (< 166.2) and intermediate-high LCRS (≥ 166.2), respectively. Never smokers with a high LCRS (≥ 21.2) had a higher 10-year cumulative incidence rate than those with a low LCRS (< 21.2; 1.05% vs. 0.22%). An online risk evaluation tool (LCKEY; http://ccra.njmu.edu.cn/lckey/web) was developed to facilitate the use of LCRS. The LCRS can be an effective risk assessment tool designed for ever and never smokers aged 30 to 80 years.

Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women.

The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.

How do women at increased risk of breast cancer make sense of their risk? An interpretative phenomenological analysis.

Offering breast cancer risk prediction for all women of screening age is being considered globally. For women who have received a clinically derived estimate, risk appraisals are often inaccurate. This study aimed to gain an in-depth understanding of women's lived experiences of receiving an increased breast cancer risk. One-to-one semi-structured telephone interviews. Eight women informed that they were at a 10-year above-average (moderate) or high risk in a breast cancer risk study (BC-Predict) were interviewed about their views on breast cancer, personal breast cancer risk and risk prevention. Interviews lasted between 40 and 70 min. Data were analysed using Interpretative Phenomenological Analysis. Four themes were generated: (i) encounters with breast cancer and perceived personal significance, where the nature of women's lived experiences of others with breast cancer impacted their views on the significance of the disease, (ii) 'It's random really': difficulty in seeking causal attributions, where women encountered contradictions and confusion in attributing causes to breast cancer, (iii) believing versus identifying with a clinically-derived breast cancer risk, where personal risk appraisals and expectations influenced women's ability to internalize their clinically derived risk and pursue preventative action and (iv) perceived utility of breast cancer risk notification, where women reflected on the usefulness of knowing their risk. Providing (numerical) risk estimates appear to have little impact on stable yet internally contradictory beliefs about breast cancer risk. Given this, discussions with healthcare professionals are needed to help women form more accurate appraisals and make informed decisions.

Analisa Gambar X-Ray Mammography dengan Convolution Neural Network pada Deep Learning dengan Arsitektur Resnet

Cancer is a disease that occurs when cells in the body undergo changes and grow uncontrollably. Breast cancer is one of the common types of cancer that affects women worldwide. Early detection of breast cancer is crucial to improve the survival rate. Mammography is a medical imaging method used for the early detection of breast cancer. In this context, deep learning technology and computerized classifiers, such as Convolutional Neural Network (CNN) with the Resnet model, have been used for the analysis and prediction of mammography images with promising results. Previous studies have shown high accuracy in classifying breast masses as benign or malignant using CNN and Resnet. Furthermore, CNN has also been employed for the classification of malignant and benign breast cancer, prediction of breast cancer risk, as well as detection and classification of breast masses with satisfactory accuracy rates. The use of deep learning in medical image analysis, including mammograms and X-ray images, has proven to be an effective tool in improving cancer diagnosis and treatment. The dataset used consisted of 322 images divided into 7 classes. After testing, an accuracy of 72% was achieved with a 90:10 ratio of test data to training data, along with the corresponding confusion matrix values. Therefore, it can be concluded that the Resnet method is capable of identifying breast cancer.

Construction and evaluation of the functional polygenic risk score for gastric cancer in a prospective cohort of the European population.

A polygenic risk score (PRS) derived from 112 single-nucleotide polymorphisms (SNPs) for gastric cancer has been reported in Chinese populations (PRS-112). However, its performance in other populations is unknown. A functional PRS (fPRS) using functional SNPs (fSNPs) may improve the generalizability of the PRS across populations with distinct ethnicities. We performed functional annotations on SNPs in strong linkage disequilibrium (LD) with the 112 previously reported SNPs to identify fSNPs that affect protein-coding or transcriptional regulation. Subsequently, we constructed an fPRS based on the fSNPs by using the LDpred2-infinitesimal model and then analyzed the performance of the PRS-112 and fPRS in the risk prediction of gastric cancer in 457,521 European participants of the UK Biobank cohort. Finally, the performance of the fPRS in combination with lifestyle factors were evaluated in predicting the risk of gastric cancer. During 4,582,045 person-years of follow-up with a total of 623 incident gastric cancer cases, we found no significant association between the PRS-112 and gastric cancer risk in the European population (hazard ratio [HR] = 1.00 [95% confidence interval (CI) 0.93-1.09], P = 0.846). We identified 125 fSNPs, including seven deleterious protein-coding SNPs and 118 regulatory non-coding SNPs, and used them to constructed the fPRS-125. Our result showed that the fPRS-125 was significantly associated with gastric cancer risk (HR = 1.11 [95% CI, 1.03-1.20], P = 0.009). Compared to participants with a low fPRS-125 (bottom quintile), those with a high fPRS-125 (top quintile) had a higher risk of incident gastric cancer (HR = 1.43 [95% CI, 1.12-1.84], P = 0.005). Moreover, we observed that participants with both an unfavorable lifestyle and a high genetic risk had the highest risk of incident gastric cancer (HR = 4.99 [95% CI, 1.55-16.10], P = 0.007) compared to those with both a favorable lifestyle and a low genetic risk. These results indicate that the fPRS-125 derived from fSNPs may act as an indicator to measure the genetic risk of gastric cancer in the European population.

Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms.

We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.

Biomarkers in Breast Cancer: An Old Story with a New End.

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.