Risk Factors, Prognostic Factors, and Nomogram for Distant Metastasis in Breast Cancer Patients Without Lymph Node Metastasis

A novel gene signature associated with anoikis predicts prognosis and unveils immune infiltration in breast cancer patients

Breast cancer, a prevalent malignancy worldwide, necessitates the identification of novel prognostic markers and therapeutic targets. This study delved into the significance of genes related to anoikis in breast cancer, with the aim of enhancing our understanding of its pathogenesis and treatment strategies. Initially, we identified differentially expressed anoikis genes in breast cancer tissues compared to normal tissues, revealing a complex landscape of gene expression. Through unsupervised clustering based on these genes, we uncovered three distinct subtypes that exhibited unique prognostic outcomes. Subsequently, utilizing LASSO and Cox regression analyses, we developed a risk score model that accurately predicted patient survival in both discovery and validation cohorts. Furthermore, we explored the functional implications of these genes and discovered associations with immune cell infiltration as well as drug sensitivity. Our analysis on drug sensitivity revealed potential antineoplastic agents that could be tailored for specific subtypes of breast cancer. In conclusion, this comprehensive analysis provides novel insights into the role played by genes related to anoikis in breast cancer and holds promise for improved prognostic assessment and targeted therapy development.

Retrospective Case-Cohort Study on Risk Factors for Developing Distant Metastases in Women With Breast Cancer

OBJECTIVE

This study aimed to identify risk factors associated with the development of metastases in breast cancer patients, to investigate survival rates, and the relationship between local recurrences and distant metastases.

METHODS

This retrospective case-cohort study included women with breast cancer who were treated at a certified Breast Unit between 2001 and 2015. Cases who developed distant metastases were compared to controls based on diagnosis year, stage, and age at diagnosis. Comprehensive information on patient characteristics, tumor biology, and treatment options was gathered.

RESULTS

The study included 412 patients who developed distant metastases and 433 controls who remained metastasis-free over a median follow-up of 150 months (interquartile range 87-202). The 20-year overall survival was 99.23% for the control group and 23.62% for those with metastasis (p < 0.01). Significant risk factors for metastasis included lobular invasive carcinoma (odds ratio (OR) 2.26, p < 0.001), triple-negative subtype (OR 4.06, p = 0.002), high tumor grade (OR 2.62, p = 0.004), larger tumor size (OR 1.02, p < 0.001), lymph node involvement (p < 0.001), and loco-regional recurrence (OR 4.32, p < 0.001). Progesterone receptor (PR) expression was protective (OR 0.52, 95% confidence interval 0.34-0.81, p = 0.003). Machine learning models supported these findings, though their clinical significance was limited.

CONCLUSIONS

Lobular invasive carcinoma, specific tumor subtypes, high grade, large tumor size, lymph node involvement, and loco-regional recurrence are all significant risk factors for distant metastasis, whereas PR expression is protective. The potential of machine learning in predicting metastasis was explored, showing promise for future personalized risk assessment.

Development and validation of predictive models for distant metastasis and prognosis of gastroenteropancreatic neuroendocrine neoplasms

Imaging examinations exhibit a certain rate of missed detection for distant metastases of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). This study aims to develop and validate a risk prediction model for the distant metastases and prognosis of GEP-NENs. This study included patients diagnosed with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. External validation was performed with patients from the China-Japan Union Hospital of Jilin University. Univariate and multivariate logistic regression analyses were conducted on the selected data to identify independent risk factors for distant metastasis in GEP-NENs. A nomogram was subsequently developed using these variables to estimate the probability of distant metastasis in patients with GEP-NENs. Subsequently, patients with distant metastasis from GEP-NENs were selected for univariate and multivariate Cox regression analyses to identify prognostic risk factors. A nomogram was subsequently developed to predict overall survival (OS) in patients with GEP-NENs. Finally, the developed nomogram was validated using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). Kaplan-Meier analysis was employed to evaluate survival differences between high-risk and low-risk groups. A total of 11,207 patients with GEP-NENs were selected from the SEER database, and 152 patients from the China-Japan Union Hospital of Jilin University were utilized as an independent external validation cohort. Univariate and multivariate logistic regression analyses revealed that the primary tumor site, tumor grade, pathological type, tumor size, T stage, and N stage are independent predictors of distant metastasis in GEP-NENs. Additionally, among the 1732 patients with distant metastasis of GEP-NENs, univariate and multivariate Cox regression analyses identified N stage, tumor size, pathological type, primary site surgery, and tumor grade as independent prognostic factors. Based on the results of the regression analyses, a nomogram model was developed. Both internal and external validation results demonstrated that the nomogram models exhibited high predictive accuracy and significant clinical utility. In summary, we developed an effective predictive model to assess distant metastasis and prognosis in GEP-NENs. This model assists clinicians in evaluating the risk of distant metastasis and in assessing patient prognosis.

Radiological and pathological predictors of post-operative upstaging of breast ductal carcinoma in situ (DCIS) to invasive ductal carcinoma and lymph-nodes metastasis; a potential algorithm for node surgical de-escalation

BACKGROUND/AIM

Ductal carcinoma in situ is considered a local disease with no metastatic potential, thus sentinel lymph node biopsy (SLNB) may be deemed an overtreatment. SLNB should be reserved for patients with invasive cancer, even though the risk of upstaging rises to 25 %. We aimed to identify clinicopathological predictors of post-operative upstaging in invasive carcinoma.

METHODS

We retrospectively analyzed patients with a pre-operative diagnosis of DCIS subjected to breast surgery between January 2017 to December 2021, and evaluated at the Breast Unit of PTV (Policlinico Tor Vergata, Rome).

RESULTS

Out of 267 patients diagnosed with DCIS, 33(12.4 %) received a diagnosis upstaging and 9(3.37 %) patients presented with sentinel lymph node (SLN) metastasis. In multivariate analysis, grade 3 tumor (OR 1.9; 95 % CI 1.2-5.6), dense nodule at mammography (OR 1.3; 95 % CI 1.1-2.6) and presence of a solid nodule at ultrasonography (OR 1.5; 95 % CI 1.2-2.6) were independent upstaging predictors. Differently, the independent predictors for SLNB metastasis were: upstaging (OR 2.1.; 95 % CI 1.2-4.6; p = 0.0079) and age between 40 and 60yrs (OR 1.4; 95 % CI 1.4-2.7; p = 0.027). All 9 patients with SLN metastasis received a diagnosis upstaging and were aged between 40 and 60 years old.

CONCLUSION

We identified pre-operative independent predictors of upstaging to invasive ductal carcinoma. The combined use of different predictors in an algorithm for surgical treatments of DCIS could reduce the numbers of unnecessary SLNB.

Risk factors, prognostic factors, and nomograms for distant metastases in patients with gastroenteropancreatic neuroendocrine tumors: a population-based study

Background

Patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) have a poor prognosis for distant metastasis. Currently, there are no studies on predictive models for the risk of distant metastasis in GEP-NETs.

Methods

In this study, risk factors associated with metastasis in patients with GEP-NETs in the Surveillance, Epidemiology, and End Results (SEER) database were analyzed by univariate and multivariate logistic regression, and a nomogram model for metastasis risk prediction was constructed. Prognostic factors associated with distant metastasis in patients with GEP-NETs were analyzed by univariate and multivariate Cox, and a nomogram model for prognostic prediction was constructed. Finally, the performance of the nomogram model predictions is validated by internal validation set and external validation set.

Results

A total of 9145 patients with GEP-NETs were enrolled in this study. Univariate and multivariate logistic analysis demonstrated that T stage, N stage, tumor size, primary site, and histologic types independent risk factors associated with distant metastasis in GEP-NETs patients (p value < 0.05). Univariate and multivariate Cox analyses demonstrated that age, histologic type, tumor size, N stage, and primary site surgery were independent factors associated with the prognosis of patients with GEP-NETs (p value < 0.05). The nomogram model constructed based on metastasis risk factors and prognostic factors can predict the occurrence of metastasis and patient prognosis of GEP-NETs very effectively in the internal training and validation sets as well as in the external validation set.

Conclusion

In conclusion, we constructed a new distant metastasis risk nomogram model and a new prognostic nomogram model for GEP-NETs patients, which provides a decision-making reference for individualized treatment of clinical patients.

Is Metastatic Staging Needed for All Patients with Synchronous Bilateral Breast Cancers?

BACKGROUND

Patients with bilateral breast cancers are uncommon and are associated with a poorer prognosis. While metastatic staging guidelines in patients with unilateral cancer were established, the indication of metastatic staging in patients with bilateral breast cancers is unclear. We aimed to determine which patients with synchronous bilateral breast cancers require metastatic staging at diagnosis. This is the first such reported study, to the best of our knowledge.

METHODS

A retrospective review of newly diagnosed synchronous bilateral invasive breast cancer patients at our institution was performed. We excluded patients with malignant phyllodes or no metastatic staging. Patients' demographics and pathological and staging results were analysed to determine the group of bilateral breast cancer patients who required metastatic staging.

RESULTS

A total of 92 patients with synchronous bilateral invasive cancers were included. The mean age was 58 years old, and 64.1% had bilateral invasive ductal carcinoma. 23.9% had systemic metastasis. Nodal status was statistically significant for systemic metastasis on staging (p = 0.0081), with only three patients (3.3%) having negative nodal status and positive metastatic staging. These three patients, however, showed symptoms of distant metastasis. 92.3% of patients with negative nodes also had negative metastatic staging. Using negative nodal status as a guide avoided metastatic staging in 40.4% of all patients.

CONCLUSIONS

Negative nodal status was the most predictive factor for no systemic metastasis on staging in patients with synchronous bilateral invasive breast cancers. Hence, metastatic staging could be reserved for patients with symptoms of systemic metastasis and/or metastatic nodes. This finding could be validated in larger studies.

Construction and Validation of a Novel Nomogram for Predicting the Risk of Metastasis in a Luminal B Type Invasive Ductal Carcinoma Population

BACKGROUND

Postoperative distant metastasis is the main cause of death in breast cancer patients. We aimed to construct a nomogram to predict the risk of metastasis of luminal B type invasive ductal carcinoma.

METHODS

We applied the data of 364 luminal B type breast cancer patients between 2008 and 2013. Patients were categorized into modeling group and validation group randomly (1:1). The breast cancer metastasis nomogram was developed from the logistic regression model using clinicopathological variables. The area under the receiver-operating characteristic curve (AUC) was calculated in modeling group and validation group to evaluate the predictive accuracy of the nomogram.

RESULTS

The multivariate logistic regression analysis showed that tumor size, No. of the positive level 1 axillary lymph nodes, human epidermal growth factor receptor 2 (HER2) status and Ki67 index were the independent predictors of the breast cancer metastasis. The AUC values of the modeling group and the validation group were 0.855 and 0.818, respectively. The nomogram had a well-fitted calibration curve. The positive and negative predictive values were 49.3% and 92.7% in the modeling group, and 47.9% and 91.0% in the validation group. Patients who had a score of 60 or more were thought to have a high risk of breast cancer metastasis.

CONCLUSIONS

The nomogram has a great predictive accuracy of predicting the risk of breast cancer metastasis. If patients had a score of 60 or more, necessary measures, like more standard treatment methods and higher treatment adherence of patients, are needed to take to lower the risk of metastasis and improve the prognosis.

Construction of novel predictive tools for post-surgical cancer-specific survival probability in patients with primary chondrosarcoma and external validation in Chinese cohorts: a large population-based retrospective study

BACKGROUND

Surgery is the predominant treatment modality for chondrosarcoma. This study aims to construct a novel clinic predictive tool that accurately predicts the 3-, 5-, and 8-year probability of cancer-specific survival (CSS) for primary chondrosarcoma patients who have undergone surgical treatment.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 982 primary chondrosarcoma patients after surgery, who were randomly divided into two sets: training set (60%) and internal validation set (40%). Cox proportional regression analyses were used to screen post-surgical independent prognostic variables in primary chondrosarcoma patients. These identified variables were used to construct a nomogram to predict the probability of post-surgical CSS of primary chondrosarcoma patients. The k-fold cross-validation method (k = 10), Harrell's concordance index (C-index), receiver operating characteristic curve (ROC) and area under curve (AUC) were used to assess the predictive accuracy of the nomogram. Calibration curve and decision curve analysis (DCA) were used to validate the clinical application of the nomogram.

RESULTS

Age, tumor size, disease stage and histological type were finally identified post-surgical independent prognostic variables. Based the above variables, a nomogram was constructed to predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. The results of the C-index showed excellent predictive performance of the nomogram (training set: 0.837, 95% CI: 0.766-0.908; internal validation set: 0.835, 95% CI: 0.733-0.937; external validation set: 0.869, 95% CI: 0.740-0.998). The AUCs of ROC were all greater than 0.830 which again indicated that the nomogram had excellent predictive performance. The results of calibration curve and DCA indicated that the clinical applicability of this nomogram was outstanding. Finally, the risk classification system and online access version of the nomogram was developed.

CONCLUSION

We constructed the first nomogram to accurately predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. This nomogram would assist surgeons to provide individualized post-surgical survival predictions and clinical strategies for primary chondrosarcoma patients.

Pectoralis muscle predicts distant metastases in breast cancer by deep learning radiomics

BACKGROUND

Sarcopenia is associated with a poor prognosis in patients with breast cancer (BC). Currently, there are few quantitative assessments carried out between muscle biomarkers and distant metastasis using existing methods.

PURPOSE

To assess the predictive value of the pectoralis muscle for BC distant metastasis, we developed a deep learning radiomics nomogram model (DLR-N) in this study.

MATERIAL AND METHODS

A total of 493 patients with pathologically confirmed BC were registered. Image features were extracted from computed tomography (CT) images for each patient. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors for distant metastasis. The DLR-N was built based on independent prognostic factors and CT images to predict distant metastases. The model was assessed in terms of overall performance, discrimination, calibration, and clinical value. Finally, the predictive performance of the model was validated using the testing cohort.

RESULTS

The developed DLR-N combined multiple radiomic features and clinicopathological factors and demonstrated excellent predictive performance. The C-index of the training cohort was 0.983 (95% confidence interval [CI] = 0.969-0.998) and the C-index of the testing cohort was 0.948 (95% CI = 0.917-0.979). Decision curve analysis (DCA) showed that patients could benefit more from incorporating multimodal radiomic features into clinicopathological models.

CONCLUSIONS

DLR-N verified that there were biomarkers at the level of the fourth thoracic vertebra (T4) that affected distant metastasis. Multimodal prediction models based on deep learning could be a potential method to aid in the prediction of distant metastases in patients with BC.

A deep-learning-based clinical risk stratification for overall survival in adolescent and young adult women with breast cancer

OBJECTIVE

The objective of this study is to construct a novel clinical risk stratification for overall survival (OS) prediction in adolescent and young adult (AYA) women with breast cancer.

METHOD

From the Surveillance, Epidemiology, and End Results (SEER) database, AYA women with primary breast cancer diagnosed from 2010 to 2018 were included in our study. A deep learning algorithm, referred to as DeepSurv, was used to construct a prognostic predictive model based on 19 variables, including demographic and clinical information. Harrell's C-index, the receiver operating characteristic (ROC) curve, and calibration plots were adopted to comprehensively assess the predictive performance of the prognostic predictive model. Then, a novel clinical risk stratification was constructed based on the total risk score derived from the prognostic predictive model. The Kaplan-Meier method was used to plot survival curves for patients with different death risks, using the log-rank test to compared the survival disparities. Decision curve analyses (DCAs) were adopted to evaluate the clinical utility of the prognostic predictive model.

RESULTS

Among 14,243 AYA women with breast cancer finally included in this study, 10,213 (71.7%) were White and the median (interquartile range, IQR) age was 36 (32-38) years. The prognostic predictive model based on DeepSurv presented high C-indices in both the training cohort [0.831 (95% CI 0.819-0.843)] and the test cohort [0.791 (95% CI 0.764-0.818)]. Similar results were observed in ROC curves. The excellent agreement between the predicted and actual OS at 3 and 5 years were both achieved in the calibration plots. The obvious survival disparities were observed according to the clinical risk stratification based on the total risk score derived from the prognostic predictive model. DCAs also showed that the risk stratification possessed a significant positive net benefit in the practical ranges of threshold probabilities. Lastly, a user-friendly Web-based calculator was generated to visualize the prognostic predictive model.

CONCLUSION

A prognostic predictive model with sufficient prediction accuracy was construct for predicting OS of AYA women with breast cancer. Given its public accessibility and easy-to-use operation, the clinical risk stratification based on the total risk score derived from the prognostic predictive model may help clinicians to make better-individualized management.

Clinical characteristics of bloodstream infections in adult patients with solid tumours and a nomogram for mortality prediction: a 5-year case-controlled retrospective study in a tertiary-level hospital

Background

Bloodstream infections (BSIs) are one of the leading causes of death in cancer patients. Nevertheless, the risk factors of BSIs in solid tumors have rarely been ascertained adequately.

Methods

We conducted a single-center case-controlled retrospective study from 2017 to 2021 among adults with solid tumors in a tertiary-level hospital. The BSIs and control group were matched by the propensity score matching method. We found independent risk factors of occurrence and death of BSIs using univariate and multivariate regression analysis. Additionally, a nomogram was constructed to predict the risk of mortality in BSIs.

Results

Of 602 patients with solid tumors in the study period, 186 had BSIs and 416 had non-BSIs. The incidence of BSIs was 2.0/1,000 admissions (206/102,704), and the 30-day mortality rate was 18.8% (35/186). Compared to the control group, the BSIs had longer hospital stays (24.5 days vs. 20.0 days), and higher frequency complicating with organ failure (10.5% vs. 2.4%), nephropathy (19.6% vs. 3.8%), comorbidities≥3 (35.5% vs. 20.0%), and liver-biliary-pancreatic infections (15.6% vs. 5.3%) (all P<0.001). Among the 186 patients with BSIs, 35 died within 30 days after BSIs. Gram-negative bacteria were the most frequent microorganisms (124/192, 64.6%). Liver cancer, organ failure, a high level of lactate dehydrogenase and septic shock were the independent hazardous factors for death of BSIs. What's more, a nomogram was constructed to predict the 30-day survival rate of BSIs, which was proved to have good accuracy (AUC: 0.854; 95% confidence interval: 0.785~0923) and consistency.

Conclusion

Being aware of the risk factors of BSIs redounds to take preventive measures to reduce the incidence and death of BSIs.

Identifying clinicopathological risk factors for regional lymph node metastasis in Chinese patients with T1 breast cancer: a population-based study

Objectives

To analyze clinicopathological risk factors and regular pattern of regional lymph node metastasis (LNM) in Chinese patients with T1 breast cancer and the effect on overall survival (OS) and disease-free survival (DFS).

Materials and methods

Between 1999 and 2020, breast cancer patients meeting inclusion criteria of unilateral, no distant metastatic site, and T1 invasive ductal carcinoma were reviewed. Clinical pathology characteristics were retrieved from medical records. Survival analysis was performed using Kaplan-Meier methods and an adjusted Cox proportional hazards model.

Results

We enrolled 11,407 eligible patients as a discovery cohort to explore risk factors for LNM and 3484 patients with stage T1N0 as a survival analysis cohort to identify the effect of those risk factors on OS and DFS. Compared with patients with N- status, patients with N+ status had a younger age, larger tumor size, higher Ki67 level, higher grade, higher HR+ and HER2+ percentages, and higher luminal B and HER2-positive subtype percentages. Logistic regression indicated that age was a protective factor and tumor size/higher grade/HR+ and HER2+ risk factors for LNM. Compared with limited LNM (N1) patients, extensive LNM (N2/3) patients had larger tumor sizes, higher Ki67 levels, higher grades, higher HR- and HER2+ percentages, and lower luminal A subtype percentages. Logistic regression indicated that HR+ was a protective factor and tumor size/higher grade/HER2+ risk factors for extensive LNM. Kaplan-Meier analysis indicated that grade was a predictor of both OS and DFS; HR was a predictor of OS but not DFS. Multivariate survival analysis using the Cox regression model demonstrated age and Ki67 level to be predictors of OS and grade and HER2 status of DFS in stage T1N0 patients.

Conclusion

In T1 breast cancer patients, there were several differences between N- and N+ patients, limited LNM and extensive LNM patients. Besides, HR+ plays a dual role in regional LNM. In patients without LNM, age and Ki67 level are predictors of OS, and grade and HER2 are predictors of DFS.

Options of locoregional therapy for primary foci of breast cancer influence the rate of nonregional lymph node metastasis in N2-N3 status patients: a SEER database analysis

OBJECTIVE

We aim to use the SEER database to discuss the effect of various surgical methods of primary foci and other influencing factors on the nonregional lymph node (NRLN) metastasis in invasive ductal carcinoma (IDC) patients.

METHODS

Clinical information of IDC patients used in this study was obtained from the SEER database. The statistical analyses used included a multivariate logistic regression model, the chi-squared test, log-rank test and propensity score matching (PSM).

RESULTS

243,533 patients were included in the analysis. 94.3% of NRLN patients had a high N positivity (N3) but an equal distribution in T status. The proportion of operation type, especially BCM and MRM, differed significantly between the N0-N1 and N2-N3 groups in the NRLN metastasis group and nonmetastasis group. Age > 80 years, positive PR, modified radical mastectomy (MRM)/radical mastectomy (RM) and radiotherapy for primary tumor were shown to be protective factors for NRLN metastasis, and higher N positivity was the most significant risk factors. N2-N3 patients receiving MRM had a lower metastasis to NRLN than those receiving BCM (1.4% vs 3.7%, P < 0.001), while this relevance was not discovered in N0-N1 patients. In N2-N3 patients, a better OS was observed in MRM group than BCM group (P < 0.001).

CONCLUSION

MRM exerted a protective effect on NRLN metastasis compared to BCM in N2-N3 patients but not N0-N1 patients. This implies the need for more consideration when choosing the operation methods of primary foci in patients with high N positivity.

A practical dynamic nomogram model for predicting bone metastasis in patients with thyroid cancer

PURPOSE

The aim of this study was to established a dynamic nomogram for assessing the risk of bone metastasis in patients with thyroid cancer (TC) and assist physicians to make accurate clinical decisions.

METHODS

The clinical data of patients with TC admitted to the First Affiliated hospital of Nanchang University from January 2006 to November 2016 were included in this study. Demographic and clinicopathological parameters of all patients at primary diagnosis were analyzed. Univariate and multivariate logistic regression analysis was applied to build a predictive model incorporating parameters. The discrimination, calibration, and clinical usefulness of the nomogram were evaluated using the C-index, ROC curve, calibration plot, and decision curve analysis. Internal validation was evaluated using the bootstrapping method.

RESULTS

A total of 565 patients were enrolled in this study, of whom 25 (4.21%) developed bone metastases. Based on logistic regression analysis, age (OR=1.040, P=0.019), hemoglobin (HB) (OR=0.947, P<0.001) and alkaline phosphatase (ALP) (OR=1.006, P=0.002) levels were used to construct the nomogram. The model exhibited good discrimination, with a C-index of 0.825 and good calibration. A C-index value of 0.815 was achieved on interval validation analysis. Decision curve analysis showed that the nomogram was clinically useful when intervention was decided at a bone metastases possibility threshold of 1%.

CONCLUSIONS

This dynamic nomogram, with relatively good accuracy, incorporating age, HB, and ALP, could be conveniently used to facilitate the prediction of bone metastasis risk in patients with TC.

The Metastatic Spread of Breast Cancer Accelerates during Sleep: How the Study Design can Affect the Results

Metastasis is the most common event that determines survival in patients with breast cancer. The benefits of appropriate sleep in enhancing cancer patients' prognosis have been demonstrated. Likewise, emerging evidence has noted the positive impacts of regular circadian rhythm on cancer survival. Proper sleep and regular circadian rhythm can help to improve the cancer prognosis by enhancing the immune system. Besides, circadian rhythm disruption can assist cancer progression by promoting systemic inflammation. However, a recent study by Diamantopoulou et al. titled "The Metastatic Spread of Breast Cancer Accelerates during Sleep" demonstrated that sleep can aggravate breast cancer metastasis. This article outlines how the study design can affect this controversy.

New nomogram for predicting lymph node positivity in pancreatic head cancer

BACKGROUND

Pancreatic cancer is one of the most malignant cancers worldwide, and it mostly occurs in the head of the pancreas. Existing laparoscopic pancreaticoduodenectomy (LPD) surgical techniques have has undergone a learning curve, a wide variety of approaches for the treatment of pancreatic cancer have been proposed, and the operation has matured. At present, pancreatic head cancer has been gradually changing from "surgeons' evaluation of anatomical resection" to "biologically inappropriate resection". In this study, the risk of lymph node metastasis in pancreatic head cancer was predicted using common preoperative clinical indicators.

METHODS

The preoperative clinical data of 191 patients with pancreatic head cancer who received LPD in the First Affiliated Hospital of Jilin University from May 2016 to December 2021 were obtained. A univariate regression analysis study was conducted, and the indicators with a significance level of P<0.05 were included in the univariate logistic regression analysis into multivariate. Lastly, a nomogram was built based on age, tumor size, leucocyte,albumin(ALB), and lymphocytes/monocytes(LMR). The model with the highest resolution was selected by obtaining the area under a curve. The clinical net benefit of the prediction model was examined using decision curve analyses.Risk stratification was performed by combining preoperative CT scan with existing models.

RESULTS

Multivariate logistic regression analysis found age, tumor size, WBC, ALB, and LMR as five independent factors. A nomogram model was constructed based on the above indicators. The model was calibrated by validating the calibration curve within 1000 bootstrap resamples. The ROC curve achieved an AUC of 0.745(confidence interval of 95%: 0.673-0.816), thus indicating that the model had excellent discriminative skills. DCA suggested that the predictive model achieved a high net benefit in the nearly entire threshold probability range.

CONCLUSIONS

This study has been the first to investigate a nomogram for preoperative prediction of lymphatic metastasis in pancreatic head cancer. The result suggests that age, ALB, tumor size, WBC, and LMR are independent risk factors for lymph node metastasis in pancreatic head cancer. This study may provide a novel perspective for the selection of appropriate continuous treatment regimens, the increase of the survival rate of patients with pancreatic head cancer, and the selection of appropriate neoadjuvant therapy patients.

Fatores Associados ao Karnofsky Performance Status e sua Trajetória no Último Mês de Vida em Pacientes com Câncer Terminal

Introdução: O Karnofsky Performance Status (KPS) pode caracterizar o impacto da doença em pacientes com câncer. Objetivo: Avaliar os fatores associados ao KPS e a sua trajetória no último mês de vida em pacientes com câncer terminal. Método: Estudo de coorte retrospectivo, com pacientes com câncer terminal internados em uma unidade de cuidados paliativos, falecidos entre julho e agosto de 2019. A variável dependente foi o KPS avaliado diariamente no último mês de vida. Uma análise transversal dos fatores associados ao KPS inicial foi realizada por meio de regressões logísticas ordinais. Para verificar a trajetória do KPS no último mês de vida, foram realizadas análises gráficas longitudinais. Resultados: Foram avaliados 108 pacientes, cuja maioria possuía >60 anos (68,5%) e era do sexo feminino (62,0%). Os sítios tumorais mais prevalentes foram o trato gastrointestinal (TGI) (24,3%), mama (18,7%) e cabeça e pescoço (CP) (16,8%). No modelo múltiplo, os sítios tumorais primários permaneceram associados ao KPS. Durante o último mês de vida, a redução do KPS foi mais pronunciada naqueles com tumor no TGI, CP e tecido ósseo conjuntivo, que apresentaram valores mais elevados de KPS no trigésimo dia antes do óbito quando comparados aos demais. Por outro lado, aqueles com câncer no sistema nervoso central e pulmão iniciaram o período de seguimento com valores de KPS mais baixos e tiveram redução menos exacerbada que os demais. Conclusão: Os valores de KPS diminuem no último mês de vida, porém com intensidade diferente de acordo com o local do tumor em pacientes com câncer terminal.

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients

Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.

Accuracy and Utility of Preoperative Ultrasound-Guided Axillary Lymph Node Biopsy for Invasive Breast Cancer: A Systematic Review and Meta-Analysis

Background

With the acceleration of the pace of life and work, the incidence rate of invasive breast cancer is getting higher and higher, and early diagnosis is very important. This study screened and analyzed the published literature on ultrasound-guided biopsy of invasive breast cancer and obtained the accuracy and practicality of preoperative biopsy.

Method

The four databases were screened for the literature. There was no requirement for the start date of retrieval, and the deadline was July 2, 2022. Two researchers screened the literature, respectively, and included the literature on preoperative ultrasound-guided biopsy and intraoperative and postoperative pathological diagnosis of invasive breast cancer. The diagnostic data included in the literature were extracted and meta-analyzed with RevMan 5.4 software, and the bias risk map, forest map, and summary receiver operating characteristic curves (SROC) were drawn.

Results

The included 19 studies involved about 18668 patients with invasive breast cancer. The degree of bias of the included literature is low. The distribution range of true positive, false positive, true negative, and false negative in the forest map is large, which may be related to the large difference in the number of patients in each study. Most studies in the SROC curve are at the upper left, indicating that the accuracy of ultrasound-guided axillary biopsy is very high.

Conclusion

For invasive breast cancer, preoperative ultrasound-guided biopsy can accurately predict staging and grading of breast cancer, which has important reference value for surgery and follow-up treatment.