Validation of the 8th edition of the American Joint Committee on Cancer Pathological Prognostic Staging for young breast cancer patients

Zhou, Juan; Lei, Jian; Wang, Jun; Lian, Chen-Lu; Li, Hua; Yang, Lichao; Wu, San‐Gang

doi:10.18632/aging.103111

Cited by 4 publications

(6 citation statements)

References 38 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our study shows that 149,452 (39.7%) individuals with BC migrated from the AJCC AS to AJCC PPS, which was similar to the rates observed in previous studies (20.7%-52.8%) [10,[18][19][20][21][22]. The downstaging rate was significantly higher than upstaging rate (33.7% vs 6.0%) in this study, and the results are consistent with those of previous studies (downstaging: 15.2%-42.1%; upstaging: 5.5%-41.0%) [10,[18][19][20][21][22]. Change in stage leads to diverse therapeutic decisions.…”

Section: Discussionsupporting

confidence: 90%

Stage-Specific Survival in Breast Cancer in Chinese and White Women: Comparative Data Analysis

Wang¹,

Zhou²,

Liu³

et al. 2022

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

Background Stage-specific survival, according to the eighth edition of the American Joint Committee on Cancer (AJCC) pathological prognostic staging (PPS) on breast cancer (BC), between Chinese and White American women remains unclear. Objective This study aimed to assess stage-specific survival in BC between Chinese and White American women according to the eighth AJCC PPS. Methods We included Chinese and White American women with BC diagnosed between 2010 and 2018 from the Surveillance, Epidemiology, and End Results database. A chi-square test, the Kaplan–Meier method, a receiver operating characteristic (ROC) curve, and multivariate Cox proportional hazards models were used for data analysis. Results We included 376,818 individuals in this study: 369,522 White American and 7296 Chinese. Of them, 149,452 (39.7%) migrated from the seventh AJCC anatomic staging (AS) to the eighth AJCC PPS, 22,516 (6.0%) were upstaged, and 126,936 (33.7%) were downstaged. With a median follow-up duration of 44 months, the 5-year overall survival and cancer-specific survival (CSS) for the entire group were 87.4% and 95.9%, respectively. The seventh AJCC AS (P<.001) and the eighth AJCC PPS (P<.001) could significantly predict the survival outcomes of BC, and multivariate analysis revealed that both staging systems were significant prognostic indicators of CSS. The ROC curve revealed that the PPS had a better discriminating ability than the AS (area under the curve [AUC] 0.769 vs 0.753, P<.001). Similar trends were observed after stratification by the 2 ethnic groups. The eighth AJCC PPS had better discriminating ability than the seventh AJCC AS among both White American (AUC 0.769 vs 0.753, P<.001) and Chinese patients (AUC 0.790 vs 0.776, P<.001). In the seventh AJCC AS, Chinese women had better CSS in stage IA (P=.02), stage IIA (P=.005), and stage IIIB (P=.04) disease than White American women, but no significant CSS was observed in stage IB, IIB, IIIA, and IIIC disease between the 2 ethnic groups. Regarding the eighth AJCC PPS, Chinese women had better CSS in stage IA (P=.002) and IIIA (P=.046) disease than White American women, and CSS was similar in Chinese and White American women in other substages. Conclusions The eighth AJCC PPS has a similar discriminative ability between White American and Chinese individuals with BC compared with the seventh AJCC AS. Therefore, the eighth AJCC PPS is also applicable to Chinese individuals with BC.

show abstract

Section: Discussionsupporting

confidence: 90%

Stage-Specific Survival in Breast Cancer in Chinese and White Women: Comparative Data Analysis

Wang¹,

Zhou²,

Liu³

et al. 2022

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

show abstract

“…The American Joint Committee on Cancer's 8th edition of the Breast Cancer TNM Staging System is widely used for staging and prognostic assessment of breast cancer [20,22]. Consistent with many previous studies, the prognosis of breast cancer patients worsens as the tumor stage advances [21,23,24]. Our research has yielded similar results; the prognosis of breast cancer patients deteriorates with the progression of tumor stages, regardless of whether the patients have undergone anti-tumor treatment.…”

Section: Discussionsupporting

confidence: 89%

Natural history and prognostic model of untreated breast cancer: a study based on the SEER database

Wang,

Yang,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

Purpose: The primary aim of this study was to explore the natural history of breast cancer in the absence of treatment, aiming to identify the main factors affecting the prognosis of untreated breast cancer patients. Methods: Data for patients diagnosed with invasive breast cancer between 2010 and 2020 were obtained from the SEER database. The disease progression time was estimated by subtracting the median survival time of higher stages from that of lower stages. Untreated patients were divided into a training set and a validation set in a 7:3 ratio. In the training set, independent risk factors affecting prognosis were identified through univariate and multivariate cox proportional hazards regression analysis. A nomogram model was constructed using these risk factors to predict the prognosis of untreated patients, which was then validated in the validation set. Results: In untreated breast cancer patients, the progression time from stage I to stage II was 43 months, from stage II to stage III was 18 months, and from stage III to stage IV was 9 months. Age, tumor stage, ER status, PR status, histological grade, and marital status were identified as independent predictive factors for the overall survival of untreated breast cancer patients, and a nomogram model was constructed with these factors. Conclusion: The disease progression speed of breast cancer patients accelerates with the increase in tumor stage in the absence of treatment, and prognosis progressively worsens. Age, tumor stage, ER status, PR status, histological grade, and marital status are independent risk factors affecting the prognosis of untreated patients.

show abstract

“…In our research, ROC curve analysis showed that the AUC value of the 7th AJCC stage was 0.683 (95%CI: 0.669–0.698, Fig 3A ), which was in the range of 0.620 to 0.728 previously studied [ 53 – 55 ]. The research conducted DeLong test on more than 60000 BC patients and found that the 10 ML algorithms had a better role in predicting the 5-year OS compared to 7th AJCC stage (all P < 0.001, Table 3 ).…”

Section: Discussionsupporting

confidence: 62%

“…For example, a study about the comparison of the prognostic accuracy of the 8th AJCC prognostic staging system to the 7th staging system using data from over 168000 BC patients confirmed the enhanced value of the 8th AJCC, while the latter still needed further improvement [ 56 ]. Furthermore, though several research results noticed that the AUC value of AJCC stage had risen from the 0.620–0.728 range of the 7th edition to the 0.670–0.773 range of the 8th edition [ 53 – 55 ], there was still a certain distance from the AUC value of MARS model in this study (AUC: 0.831, 95%CI: 0.820–0.842).…”

Section: Discussioncontrasting

confidence: 51%

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database

Luo

Wan

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Introduction Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance. Objective The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others. Methods This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database. Results Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820–0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P < 0.05). MARS, the best performing model, was selected for web application development (https://w12251393.shinyapps.io/app2/). Conclusions The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.

show abstract

Validation of the 8th edition of the American Joint Committee on Cancer Pathological Prognostic Staging for young breast cancer patients

Cited by 4 publications

References 38 publications

Stage-Specific Survival in Breast Cancer in Chinese and White Women: Comparative Data Analysis

Stage-Specific Survival in Breast Cancer in Chinese and White Women: Comparative Data Analysis

Natural history and prognostic model of untreated breast cancer: a study based on the SEER database

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database

Contact Info

Product

Resources

About