Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model

BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

Risk factors and survival prediction model establishment for prognosis in patients with radical resection of gallbladder cancer

BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.

Navigating breast cancer brain metastasis:Risk factors,prognostic indicators,and treatment perspectives

In this editorial,we comment on the article by Chen et al.We specifically focus on the risk factors,prognostic factors,and management of brain metastasis(BM)in breast cancer(BC).BC is the second most common cancer to have BM after lung cancer.Independent risk factors for BM in BC are:HER-2 positive BC,triplenegative BC,and germline BRCA mutation.Other factors associated with BM are lung metastasis,age less than 40 years,and African and American ancestry.Even though risk factors associated with BM in BC are elucidated,there is a lack of data on predictive models for BM in BC.Few studies have been made to formulate predictive models or nomograms to address this issue,where age,grade of tumor,HER-2 receptor status,and number of metastatic sites(1 vs>1)were predictive of BM in metastatic BC.However,none have been used in clinical practice.National Comprehensive Cancer Network recommends screening of BM in advanced BC only when the patient is symptomatic or suspicious of central nervous system symptoms;routine screening for BM in BC is not recommended in the guidelines.BM decreases the quality of life and will have a significant psychological impact.Further studies are required for designing validated nomograms or predictive models for BM in BC;these models can be used in the future to develop treatment approaches to prevent BM,which improves the quality of life and overall survival.

A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity

Objective:Neoadjuvant therapy(NAT)has been widely implemented as an essential treatment to improve therapeutic efficacy in patients with locally-advanced cancer to reduce tumor burden and prolong survival,particularly for human epidermal growth receptor 2-positive and triple-negative breast cancer.The role of peripheral immune components in predicting therapeutic responses has received limited attention.Herein we determined the relationship between dynamic changes in peripheral immune indices and therapeutic responses during NAT administration.Methods:Peripheral immune index data were collected from 134 patients before and after NAT.Logistic regression and machine learning algorithms were applied to the feature selection and model construction processes,respectively.Results:Peripheral immune status with a greater number of CD3^(+)T cells before and after NAT,and a greater number of CD8^(+)T cells,fewer CD4^(+)T cells,and fewer NK cells after NAT was significantly related to a pathological complete response(P<0.05).The post-NAT NK cell-to-pre-NAT NK cell ratio was negatively correlated with the response to NAT(HR=0.13,P=0.008).Based on the results of logistic regression,14 reliable features(P<0.05)were selected to construct the machine learning model.The random forest model exhibited the best power to predict efficacy of NAT among 10 machine learning model approaches(AUC=0.733).Conclusions:Statistically significant relationships between several specific immune indices and the efficacy of NAT were revealed.A random forest model based on dynamic changes in peripheral immune indices showed robust performance in predicting NAT efficacy.

Predicting lymph node metastasis in colorectal cancer:An analysis of influencing factors to develop a risk model

BACKGROUND Colorectal cancer(CRC)is a significant global health issue,and lymph node metastasis(LNM)is a crucial prognostic factor.Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC.However,the prediction of LNM is challenging and depends on various factors such as tumor histology,clinicopathological features,and molecular characteristics.The most reliable method to detect LNM is the histopathological examination of surgically resected specimens;however,this method is invasive,time-consuming,and subject to sampling errors and interobserver variability.AIM To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.METHODS This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021.A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.RESULTS The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables.The model achieved high accuracy(0.86),sensitivity(0.81),specificity(0.87),positive predictive value(0.66),negative predictive value(0.94),area under the curve for the receiver operating characteristic(0.91),and a low Brier score(0.10).The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds,indicating good calibration and clinical utility.CONCLUSION The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC.This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables,demonstrating superior performance and clinical applicability compared to existing models.The study provides new insights into the potential of deep learning to extract valuable information from tumor histology,in turn,improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.

An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population:A prospective cohort and modeling study

Background:Breast cancer(BC)risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking.We aimed to develop risk-stratification models to predict long-and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.Methods:The Breast Cancer Cohort Study in Chinese Women,a large ongoing prospective dynamic cohort study,includes 122,058 women aged 25-70 years old from the eastern part of China.We developed multiple machine-learning risk prediction models using parametric models(penalized logistic regression,bootstrap,and ensemble learning),which were the short-term ensemble penalized logistic regression(EPLR)risk prediction model and the ensemble penalized long-term(EPLT)risk prediction model to estimate BC risk.The models were assessed based on calibration and discrimination,and following this assessment,they were externally validated in new study participants from 2017 to 2020.Results:The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set.For the long-term EPLT risk prediction model,the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations,respectively.The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model(HCBCP)models for external validation was 0.193 and 0.233,respectively,indicating that the EPLT model has higher classification accuracy.Conclusions:We developed the EPLR and EPLT models to screen populations with a high risk of developing BC.These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy

BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Tumor-Immune Interaction System with the Effect of Time Delay and Hyperglycemia on the Breast Cancer Cells

Breast cancer in women is a complicated and multifaceted disease. Studies have demonstrated that hyperglycemia is one of the most significant risk factors for breast cancer. Hyperglycemia is when the sugar level in human blood is too high, which means excess glucose. Glucose excess can encourage the growth, invasion, and migration of breast cancer cells at the cellular level. Though, the effects of glucose on the dynamics of breast cancer cells have been examined mathematically by a system of ordinary differential equations. However, the non-instantaneous biological occurrences leading to the secretion of immuno-suppressive cytokines by tumors to evade immune surveillance and the immune cells’ derivation of cytokines to attack the tumor cells are not yet discussed. Therefore, investigating the biological process involved in the dynamics of tumors, immune and normal cells with excessive glucose concentration is inviolable to determining the best procedure for controlling tumors’ uncontrollable growth. Time delay, denoted by τ, is used to describe the time tumor cells take to secrete immunosuppressive cytokines to evade immune surveillance and the time immune cells take to recognize and attack the tumor cells. We have studied the local stability analysis of the biological steady states in both delayed and non-delayed system. The Routh-Hurwitz stability criterion is used to analyze the dynamical equilibrium of the cells’ population. Hopf bifurcation was analyzed by using time delay s as a bifurcation parameter. The analytical results suggest an unstable scenario for a tumor-free equilibrium point as normal cells are bound to grow to their carrying capacity. The result predicts a stable system for coexisting equilibrium when the interaction is instantaneous (τ = 0). However, when τ > 0, the coexisting equilibrium point switches from stable to unstable. The numerical results not only validate all the analytical results but also show the case of possible situations when glucose concentration is varied, indicating that both tumor growth and immune system efficiency are highly affected by the level of glucose in the blood. This concluded that the delay in the secretion of cytokines by immune cells and derivation cytokines by the tumors helps to identify the possible chaotic situation under different glucose concentration and the extent to which such delay can have on restoration of the normal cells when glucose concentration is low.

Reducing the Range of Cancer Risk on BI-RADS 4 Subcategories via Mathematical Modelling

Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories.Mathematicalmodels play an important role in the diagnosis and treatment of cancer.In this study,data of 42 BI-RADS 4 patients taken fromthe Center for Breast Health,Near East University Hospital is utilized.Regarding the analysis,a mathematical model is constructed by dividing the population into 4 compartments.Sensitivity analysis is applied to the parameters with the desired outcome of a reduced range of cancer risk.Numerical simulations of the parameters are demonstrated.The results of the model have revealed that an increase in the lactation rate and earlymenopause have a negative correlation with the chance of being diagnosed with BI-RADS 4 whereas a positive correlation increase in age,the palpable mass,and family history is distinctive.Furthermore,the negative effects of smoking and late menopause on BI-RADS 4C diagnosis are vehemently outlined.Consequently,the model showed that the percentages of parameters play an important role in the diagnosis of BI-RADS 4 subcategories.All things considered,with the assistance of the most effective parameters,the range of cancer risks in BI-RADS 4 subcategories will decrease.

Competing Risks Analysis of African American Breast Cancer Patients

Purpose: Recent studies showed that African Americans (AA) breast cancer patients experience lower survival than any other race. The knowledge of cause-specific survival of such patients is necessary to investigate the different factors associated with the disease and support the clinical practice. Methods: The parametric competing risk method is applied to build up the survival models and the parametric mixture model is used to study the overall survival of these patients. The Kaplan-Meier survival estimation is also computed to compare the results. Results: The overall death rate decreases sharply immediately after the diagnosis and increases thereafter. The risk of death from breast cancer itself is the highest at the first five years;other causes, however, pose more threats to patients after this period. The patients who received only surgery have higher survival rate in long run. The use of radiation only does not have the significant effect on patients’ survival. Conclusion: Our study shows that the parametric competing risk models are promising in estimating the cause-specific survival of AA breast cancer patients and can be used for clinical practice. We also observed that heart and other diseases pose more threat to breast cancer patients in the long run.

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients

Background: Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. Methods: In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). Results: A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. Conclusions: A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.

Polygenic risk score for prediction of radiotherapy efficacy and radiosensitivity in patients with non-metastatic breast cancer

Objective:To construct a novel polygenic risk scoring model,in order to predict the benefits of radiosensitivity in patients with non-metastatic breast cancer(NMBC).Methods:A total of 450 NMBC patients from The Cancer Genome Atlas(TCGA)were enrolled and randomly assigned 6:4(training vs.validation).The empirical Bayes differential analysis was used to perform differential expression analysis,univariate Cox regression and Kaplan-Meier analysis were used to screen for prognosisrelated genes.Finally,LASSO regression and stepwise regression were used to select key prognostic-related genes.We constructed a multivariate Cox proportional risk regression model using key genes.The pRRophetic function was used to predict drug sensitivity of radiosensitivity(RS)and radioresistance(RR)groups for adjuvant therapy.Results:Eight genes(AMH,H2BU1,HOXB13,TMEM132A,TMEM270,ODF3L1,RIIAD1 and RIMBP2)were screened to build a polygenic risk scoring model.The region of characteristic(ROC)curves were drawn based on the 3-,5-and 10-year overall survival(OS),with area under curves(AUCs)of 0.816,0.822 and 0.806,respectively.RS and RR can be effectively distinguished according to the risk score of 2.004.Gene set enrichment analysis(GSEA)showed that necroptosis was significantly enriched in RS,while complement and coagulation cascade,JAK-STAT and PPAR signaling pathways were significantly enriched in RR.Alternatively,for those radioresistant patients,the chemotherapy drugs that might be more helpful are Cisplatin,Docetaxel,Methotrexate and Vinblastine with higher drug sensitivity.Conclusion:The polygenic risk scoring model showed prediction for the benefit of radiotherapy in NMBC patients,which might be used to guide clinical practice.

Multicenter study of the clinicopathological features and recurrence risk prediction model of early-stage breast cancer with low-positive human epidermal growth factor receptor 2 expression in China (Chinese Society of Breast Surgery 021)

Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors.Methods:Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association,from January 2015 to December 2016 were collected.The prognostic factors of these patients were analyzed.Results:Twenty-nine hospitals provided valid cases.From 2015 to 2016,a total of 25,096 cases of early-stage breast cancer were treated,7642(30.5%)of which had low-positive HER2 expression and were included in the study.After ineligible cases were excluded,6486 patients were included in the study.The median follow-up time was 57 months(4-76 months).The disease-free survival rate was 92.1%at 5 years,and the overall survival rate was 97.4%at 5 years.At the follow-up,506(7.8%)cases of metastasis and 167(2.6%)deaths were noted.Multivariate Cox regression analysis showed that tumor stage,lymphvascular invasion,and the Ki67 index were related to recurrence and metastasis(P<0.05).The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815(95%confidence interval:0.750-0.880).Conclusions:Early-stage breast cancer patients with low-positive HER2 expression account for 30.5%of all patients.Tumor stage,lymphvascular invasion,and the Ki67 index are factors affecting prognosis.The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years.Trial registration:ChiCTR.org.cn,ChiCTR2100046766.

Ultrasound Features Improve Diagnostic Performance of Ovarian Cancer Predictors in Distinguishing Benign and Malignant Ovarian Tumors

To determine whether ultrasound features can improve the diagnostic performance of tumor markers in distinguishing ovarian tumors,we enrolled 719 patients diagnosed as having ovarian tumors at Nanfang Hospital from September 2014 to November 2016.Age,menopausal status,histopathology,the International Federation of Gynecology and Obstetrics(FIGO)stages,tumor biomarker levels,and detailed ultrasound reports of patients were collected.The area under the curve(AUC),sensitivity,and specificity of the bellow-mentioned predictors were analyzed using the receiver operating characteristic curve.Of the 719 patients,531 had benign lesions,119 had epithelial ovarian cancers(EOC),44 had borderline ovarian tumors(BOT),and 25 had non-EOC.AUCs and the sensitivity of cancer antigen 125(CAI25),human epididymis-specific protein 4(HE4),Risk of Ovarian Malignancy Algorithm(ROMA),Risk of Malignancy Index(RMI1),HE4 model,and Rajavithi-Ovarian Cancer Predictive Score(R-OPS)in the overall population were 0.792,0.854,0.856,0.872,0.893,0.852,and 70.2%,56.9%,69.1%,60.6%,77.1%,71.3%,respectively.For distinguishing EOC from benign tumors,the AUCs and sensitivity of the above mentioned predictors were 0.888,0.946,0.947,0.949,0.967,0.966,and 84.0%,79.8%,87.4%,84.9%,90.8%,89.1%,respectively.Their specificity in predicting benign diseases was 72.9%,94.4%,87.6%,95.9%,86.3%,90.8%,respectively.Therefore,we consider biomarkers in combination with ultrasound features may improve the diagnostic performance in distinguishing malignant from benign ovarian tumors.

Preoperative prediction of lymph node metastasis using deep learning-based features

Lymph node involvement increases the risk of breast cancer recurrence.An accurate non-invasive assessment of nodal involvement is valuable in cancer staging,surgical risk,and cost savings.Radiomics has been proposed to pre-operatively predict sentinel lymph node(SLN)status;however,radiomic models are known to be sensitive to acquisition parameters.The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based(DLB)features and compare its predictive performance to state-of-the-art radiomics.Specifically,this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution.Dynamic contrast-enhancement images from 198 patients(67 positive SLNs)were used in this study.Of these subjects,163 had an in-plane resolution of 0.7×0.7 mm^(2),which were randomly divided into a training set(approximately 67%)and a validation set(approximately 33%).The remaining 35 subjects with a different in-plane resolution(0.78×0.78 mm^(2))were treated as independent testing set for generalizability.Two methods were employed:(1)conventional radiomics(CR),and(2)DLB features which replaced hand-curated features with pre-trained VGG-16 features.The threshold determined using the training set was applied to the independent validation and testing dataset.Same feature reduction,feature selection,model creation procedures were used for both approaches.In the validation set(same resolution as training),the DLB model outperformed the CR model(accuracy 83%vs 80%).Furthermore,in the independent testing set of the dissimilar resolution,the DLB model performed markedly better than the CR model(accuracy 77%vs 71%).The predictive performance of the DLB model outperformed the CR model for this task.More interestingly,these improvements were seen particularly in the independent testing set of dissimilar resolution.This could indicate that DLB features can ultimately result in a more generalizable model.

Effect of low anterior resection syndrome on quality of life in colorectal cancer patients:A retrospective observational study

BACKGROUND Low anterior resection syndrome(LARS)is a common complication of anuspreserving surgery in patients with colorectal cancer,which significantly affects patients'quality of life.AIM To determine the relationship between the incidence of LARS and patient quality of life after colorectal cancer surgery and to establish a LARS prediction model to allow perioperative precision nursing.METHODS We reviewed the data from patients who underwent elective radical resection for colorectal cancer at our institution from April 2013 to June 2020 and completed the LARS score questionnaire and the European Organization for Research and Treatment of Cancer Core Quality of Life and Colorectal Cancer Module questionnaires.According to the LARS score results,the patients were divided into no LARS,mild LARS,and severe LARS groups.The incidence of LARS and the effects of this condition on patient quality of life were determined.Univariate and multivariate analyses were performed to identify independent risk factors for the occurrence of LARS.Based on these factors,we established a risk prediction model for LARS and evaluated its performance.RESULTS Among the 223 patients included,51 did not develop LARS and 171 had mild or severe LARS.The following quality of life indicators showed significant differences between patients without LARS and those with mild or severe LARS:Physical,role,emotional,and cognitive function,total health status,fatigue,pain,shortness of breath,insomnia,constipation,and diarrhea.Tumor size,partial/total mesorectal excision,colostomy,preoperative radiotherapy,and neoadjuvant chemotherapy were identified to be independent risk factors for LARS.A LARS prediction model was successfully established,which demonstrated an accuracy of 0.808 for predicting the occurrence of LARS.CONCLUSION The quality of life of patients with LARS after colorectal cancer surgery is significantly reduced.

Breast and Ovarian Cancer in Young Women of the Arabian Gulf Region: Relationship to Age

It is widely known that cancer is a disease of “old-age”. However available data show that this is not the case for many types of cancers. Incidences of breast and ovarian cancers have varying rates of change with age. Breast cancer data of Arabian-gulf women, show that the incidence rates increase with age and reach a maximum at 39 year. It then declines linearly with age to about 55 years. The rate of increase and its changes with age are similar to those of many other countries. In the premenopausal phase the relationship between incidence and age could be adequately modeled using a linear model for the logarithmic transformations of age and incidence. Similar observations are made for the ovarian cancer incidences. Results: It is shown that the rate of increase in breast and ovarian cancer incidence with respect to age is increasing in the premenopausal ages. Moreover, the burden of the disease with respect to mortality and “Disability Adjusted Life Years” or DALY, varied considerably among the six gulf countries. Conclusions: We conclude, based on the age incidence relationship that the number of cancer cases may double in the next period that follows our study period (1998-2009). Moreover, if the six countries have identical relationship between age and the two types of cancer, there should be an integrated and unified effort to have a common strategy for prevention and control.

结直肠癌患者卡培他滨化疗相关不良反应的风险评估及风险预测模型构建

目的探究结直肠癌(CRC)患者卡培他滨化疗诱导不良反应(CIAEs)的危险因素并构建CIAEs发生的风险预测模型。方法回顾性收集2021年1月至2023年12月于温州医科大学附属第一医院接受卡培他滨片治疗的根治性术后CRC患者临床资料。根据是否出现CIAEs分为CIAEs和非CIAEs(NCIAEs)组。采用t检验和卡方检验筛选差异变量。多因素Logistic回归探究CRC患者发生CIAEs的独立影响因素,并使用R软件构建CRC患者CIAEs风险预测模型。采用受试者工作特征曲线分析、校准曲线和决策曲线评估CIAEs发生风险预测模型的预测能力、校准能力和临床净获益。结果本研究共纳入253例接受卡培他滨片治疗的根治性术后CRC患者,201例发生CIAEs,恶心呕吐(69.96%)发生率最高。多因素Logistic回归结果表明,年龄[OR=3.018,95%CI(1.404,6.487),P=0.005]、预后营养指数[OR=0.129,95%CI(0.06,0.278),P<0.001]和系统炎症指数[OR=4.074,95%CI(1.316,12.615),P=0.015]是CRC患者发生CIAEs的独立影响因素。本研究所构建的CIAEs发生风险预测模型具有良好的预测能力、校准能力和临床净获益。结论CIAEs发生风险预测模型可用于CRC患者CIAEs的个体化预测,是预防CIAEs和护理管理过程中一种简单实用的工具。

基于对比增强CT的影像组学模型可预测乳腺癌患者的肿瘤浸润淋巴细胞水平

目的:基于对比增强CT开发用于预测乳腺癌(BC)患者肿瘤浸润淋巴细胞(TIL)水平的新型影像组学模型。方法:回顾性纳入210名非特殊浸润性BC患者(训练集:147名,验证集:63名),使用Pyradiomics软件包提取患者对比增强CT图像中的高维影像组学特征,然后利用Mann-Whitney U检验、斯皮尔曼相关系数和最小绝对值收敛和选择算子(LASSO)算法进行特征逐步筛选。筛选后的最优特征通过非线性支持向量机(NLSVM)方法在训练集中开发了影像组学模型,并在验证集中对模型的判别能力进行验证。之后利用沙普利加和解释(SHAP)算法对模型进行全局解释与特征重要性排序。结果:模型训练组和验证组的曲线下面积(AUC)分别为0.824(95%CI:0.762~0.886)和0.766(95%CI:0.624~0.909),影像组学模型有3个正向影响特征与4个负向影响特征,其中log-sigma-5-0-mm-3D_firstorder_Maximum对模型的影响力最大,其值越大时,模型输出SHAP值越小。结论:基于对比增强CT的影像组学模型可以帮助临床医生在治疗前准确预测BC患者的肿瘤浸润淋巴细胞水平,促进BC患者的个性化治疗。