Development and Validation a Survival Prediction Model and a Risk Stratification for Elderly Locally Advanced Breast Cancer

Meng, Xiangdi; Chang, Xiaolong; Wang, Xiaoxiao; Guo, Yinghua

doi:10.1016/j.clbc.2022.06.002

Cited by 5 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the DCA curve, our two nomograms exhibited a good degree of clinical benefit than TNM staging. Currently, a nomogram is known to be developed and applied to elderly LABC patients, such as Meng et al ( 41 ), who included 10,697 elderly LABC patients from 2010 to 2017 in the SEER database and developed an OS prediction model for elderly LABC to assist in patient treatment and follow-up. With the update of the SEER database and the improvement of patient clinical information, this study included 22,842 LABC patients from 2010 to 2015, selected more patient clinical characteristics, and then constructed OS and CSS prediction models applicable to LABC patients of all ages.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study

Yin

Wang

Hou

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

BackgroundFor patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.MethodsA retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsThe LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.ConclusionTwo prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study

Yin

Wang

Hou

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…It considered several key predictors to quantify risk and improve predictive accuracy [ 18 , 19 ], thus providing a more accurate prognostic estimate than N staging, which only considered the number of pLNs. In addition, the nomogram was reportedly prevalent in various cancer studies and was considered superior to the TNM system alone [ 20 , 21 ]. Assuming LODDS acts as an independent prognostic factor for breast IMPC, its inclusion in a nomogram could enhance both performance and clinical utility.…”

Section: Introductionmentioning

confidence: 99%

Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast

Meng,

Hao,

Wang

et al. 2024

BMC Med Res Methodol

Self Cite

View full text Add to dashboard Cite

Background Invasive micropapillary carcinoma (IMPC) of the breast is known for its high propensity for lymph node (LN) invasion. Inadequate LN dissection may compromise the precision of prognostic assessments. This study introduces a log odds of positive lymph nodes (LODDS) method to address this issue and develops a novel LODDS-based nomogram to provide accurate prognostic information. Methods The study analyzed data from 1,901 patients with breast IMPC from the Surveillance, Epidemiology, and End Results database. It assessed the relationships between LODDS and the number of excised LN (eLN), positive LN (pLN), and the pLN ratio (pLNR), identifying an optimal threshold value using a restricted cubic spline method. Predictive factors were identified by the Cox least absolute shrinkage and selection operator (Cox-LASSO) regression and validated through multivariate Cox regression to construct a nomogram. The model's accuracy, discrimination, and utility were assessed. The study also explored the consequences of excluding LODDS from the nomogram and compared its effectiveness with the tumor-node-metastasis (TNM) staging system. Results LODDS improved N status classification by identifying heterogeneity in patients with pLN ratios of 0% (pLN =0) or 100% (pLN =eLN) and setting -1.08 as the ideal cutoff. Five independent prognostic factors for breast cancer-specific survival (BCSS) were identified: tumor size, N status, LODDS, progesterone receptor status, and histological grade. The LODDS-based nomogram achieved a strong concordance index of 0.802 (95% CI: 0.741-0.863), surpassing both the version without LODDS and the conventional TNM staging in all tests. Conclusions For breast IMPC, LODDS served as an independent prognostic factor, its effectiveness unaffected by the anatomical LN count, enhancing the accuracy of N staging. The LODDS-based nomogram showed promise in offering more personalized prognostic information.

show abstract

“…[7][8][9] The standard of care to treat LABC involves neoadjuvant chemotherapy (NAC) followed by surgery and in select cases, adjuvant therapies, such as radiotherapy, endocrine therapy and targeted drugs. 10,11 Despite multimodal treatment plans, LABC patients exhibit low overall survival,and outcomes are highly dependent on tumor response to NAC 12 , thus pathological response is correlated to survival. 13 However, only about 30% of LABC patients achieve a pathological complete response (pCR) to NAC.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical self‐attention‐guided deep learning framework to predict breast cancer response to chemotherapy using pre‑treatment tumor biopsies

2023

View full text Add to dashboard Cite

BackgroundPathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Early prediction of NAC response would facilitate therapeutic modifications for individual patients, potentially improving overall treatment outcomes and patient survival.PurposeThis study, for the first time, proposes a hierarchical self‐attention‐guided deep learning framework to predict NAC response in breast cancer patients using digital histopathological images of pre‐treatment biopsy specimens.MethodsDigitized hematoxylin and eosin‐stained slides of BC core needle biopsies were obtained from 207 patients treated with NAC, followed by surgery. The response to NAC for each patient was determined using the standard clinical and pathological criteria after surgery. The digital pathology images were processed through the proposed hierarchical framework consisting of patch‐level and tumor‐level processing modules followed by a patient‐level response prediction component. A combination of convolutional layers and transformer self‐attention blocks were utilized in the patch‐level processing architecture to generate optimized feature maps. The feature maps were analyzed through two vision transformer architectures adapted for the tumor‐level processing and the patient‐level response prediction components. The feature map sequences for these transformer architectures were defined based on the patch positions within the tumor beds and the bed positions within the biopsy slide, respectively. A five‐fold cross‐validation at the patient level was applied on the training set (144 patients with 9430 annotated tumor beds and 1,559,784 patches) to train the models and optimize the hyperparameters. An unseen independent test set (63 patients with 3574 annotated tumor beds and 173,637 patches) was used to evaluate the framework.ResultsThe obtained results on the test set showed an AUC of 0.89 and an F1‐score of 90% for predicting pCR to NAC a priori by the proposed hierarchical framework. Similar frameworks with the patch‐level, patch‐level + tumor‐level, and patch‐level + patient‐level processing components resulted in AUCs of 0.79, 0.81, and 0.84 and F1‐scores of 86%, 87%, and 89%, respectively.ConclusionsThe results demonstrate a high potential of the proposed hierarchical deep‐learning methodology for analyzing digital pathology images of pre‐treatment tumor biopsies to predict the pathological response of breast cancer to NAC.

show abstract

Development and Validation a Survival Prediction Model and a Risk Stratification for Elderly Locally Advanced Breast Cancer

Cited by 5 publications

References 27 publications

Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study

Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study

Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast

A hierarchical self‐attention‐guided deep learning framework to predict breast cancer response to chemotherapy using pre‑treatment tumor biopsies

Contact Info

Product

Resources

About