Comparison of Breast Cancer Staging Systems After Neoadjuvant Chemotherapy

Kantor, Olga; Laws, Alison; Pastorello, Ricardo; King, Claire; Wong, Stéphanie; Dey, Tanujit; Schnitt, Stuart J.; King, Tari A.; Mittendorf, Elizabeth A.

doi:10.1245/s10434-021-09951-7

Cited by 5 publications

(14 citation statements)

References 23 publications

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“…16 However, a study conducted by Dana-Farber Cancer Institute showed that RCB is superior to the Neo-Bioscore for stratifying patients into different survival outcomes. 17 Intriguingly, the AUROC values of five models applied to the patients in our center were inferior to those of the model. This is likely because of the distribution difference of heterogeneity in different races, the discrepancy of NAC therapeutic guidelines in different countries, and the predictive improvement of our model after including RCB and LVI.…”

Section: Risk Stratification Of the Modelmentioning

confidence: 75%

“…AJCC stage, grade, ER expression, HER2 status and treatment, Ki-67 expression, and LVI have been determined as independent predictors in BC patients who received NAC. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] T stage and N stage, which indicate primary and metastatic lymph node burden, respectively, have been seen as predictors in several studies. 15,18 The model we constructed incorporated AJCC stage, which takes together these two factors.…”

Section: Risk Stratification Of the Modelmentioning

confidence: 99%

“…This is likely because of the distribution difference of heterogeneity in different races, the discrepancy of NAC therapeutic guidelines in different countries, and the predictive improvement of our model after including RCB and LVI. 16,17 The optimal target population for NAC has been explored and determined in several randomized clinical trials, but verification regarding its prognostic difference is deficient. This may produce unrecognized confounding which might potentially affect the results of these studies, therefore a risk stratification model for discriminating and diminishing heterogeneity is urgently needed.…”

Section: Risk Stratification Of the Modelmentioning

confidence: 99%

“…[13][14][15] However, almost all these studies are seemly dated because human epidermal growth factor receptor 2 (HER2)-targeted therapies and informative pathological measurement were not taken into consideration in that case. [6][7][8][9][10][11][12]16,17 A nomogram created for the population in the recent clinical guidelines is therefore urgently required and comparisons of the novel model with previous models are crucial.…”

Section: Introductionmentioning

confidence: 99%

“…Basic and predictive parameters for formulating a model specific to patients with BC encompass clinical stage, histology type, grade, the expression of ER, progesterone receptor (PR), Ki-67, and HER2, residual tumor burden [including pathological complete remission (pCR) status, pathological AJCC stage], and lymphovascular invasion (LVI). [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] Among these indicators, residual tumor appears especially important not only because it shows a negative correlation with prognosis but also its cancer burden ranges from single cancer cell to large diameters of tumor in these patients, which, in part, could lead to a great degree of heterogeneity in BC population after NAC. Several randomized studies and meta-analysis demonstrated that patients achieving pCR during the NAC have longer diseasefree survival (DFS) and overall survival (OS) than those with residual cancer.…”

Section: Introductionmentioning

confidence: 99%

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Development, verification, and comparison of a risk stratification model integrating residual cancer burden to predict individual prognosis in early-stage breast cancer treated with neoadjuvant therapy

Hou

Xiao

et al. 2021

ESMO Open

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Background A favorable model for predicting disease-free survival (DFS) and stratifying prognostic risk in breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) is lacking. The aim of the current study was to formulate an excellent model specially for predicting prognosis in these patients. Patients and methods Between January 2012 and December 2015, 749 early-stage BC patients who received NAC in Xijing hospital were included. Patients were randomly assigned to a training cohort ( n = 563) and an independent cohort ( n = 186). A prognostic model was created and subsequently validated. Predictive performance and discrimination were further measured and compared with other models. Results Clinical American Joint Committee on Cancer stage, grade, estrogen receptor expression, human epidermal growth factor receptor 2 (HER2) status and treatment, Ki-67 expression, lymphovascular invasion, and residual cancer burden were identified as independent prognostic variables for BC treated with NAC. The C-index of the model consistently outperformed other available models as well as single independent factors with 0.78, 0.80, 0.75, 0.82, and 0.77 in the training cohort, independent cohort, luminal BC, HER2-positive BC, and triple-negative BC, respectively. With the optimal cut-off values (280 and 360) selected by X-tile, patients were categorized as low-risk (total points ≤280), moderate-risk (280 < total points ≤ 360), and high-risk (total points >360) groups presenting significantly different 5-year DFS of 89.9%, 56.9%, and 27.7%, respectively. Conclusions In patients with BC, the first model including residual cancer burden index was demonstrated to predict the survival of individuals with favorable performance and discrimination. Furthermore, the risk stratification generated by it could determine the risk level of recurrence in whole early-stage BC cohort and subtype-specific cohorts, help tailor personalized intensive treatment, and select comparable study cohort in clinical trials.

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Section: Risk Stratification Of the Modelmentioning

confidence: 75%

Section: Risk Stratification Of the Modelmentioning

confidence: 99%

Section: Risk Stratification Of the Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development, verification, and comparison of a risk stratification model integrating residual cancer burden to predict individual prognosis in early-stage breast cancer treated with neoadjuvant therapy

Hou

Xiao

et al. 2021

ESMO Open

View full text Add to dashboard Cite

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Evaluation of the relationship between Ki67 expression level and neoadjuvant treatment response and prognosis in breast cancer based on the Neo-Bioscore staging system

Sullu,

Tomak,

Demirag

et al. 2023

Discov Onc

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Background Neoadjuvant chemotherapy (NAC) is widely used in the treatment of primary breast cancer. Different staging systems have been developed to evaluate the residual tumor after NAC and classify patients into different prognostic groups. Ki67, a proliferation marker, has been shown to be useful in predicting treatment response and prognosis. We aimed to investigate the prognostic importance Neo-Bioscore stage and pretreatment and posttreatment Ki67 levels in breast cancer patients who received NAC and correlations between Neo-Bioscore stage and pretreatment and posttreatment Ki67 levels. Methods A total of 176 invasive breast carcinoma patients who underwent NAC were included in the study. Ki67 levels were evaluated by immunohistochemical methods in Trucut biopsy and surgical excision specimens. Patients were classified into prognostic groups using the Neo-Bioscore staging system. Results Patients with high pretreatment Ki67 score were more likely to be in the higher Neo-Bioscore risk group (p < 0.001). Patients with a high posttreatment Ki67 score were more likely to be in the higher Neo-Bioscore prognostic risk group (p < 0.001). Overall survival (OS) and disease-free survival (DFS) were shorter in patients with high posttreatment Ki67 scores and in patients in the higher Neo-Bioscore risk group. We also determined a cutoff 37% for pathological complete response. Conclusion Neo-Bioscore staging system is found to be important in predicting survival. The posttreatment Ki67 level is more important than pretreatment Ki67 level in predicting survival.

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Pathological measurement and staging of residual breast cancer after neoadjuvant chemotherapy

et al. 2023

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AimsThe 8th Edition of the American Joint Committee on Cancer Staging System (yAJCC) excludes treatment‐related fibrosis from the measurement of residual tumour after neoadjuvant chemotherapy (NAC) for breast cancer. The impact of the 8th Ed. yAJCC on post‐NAC pathologic staging was examined in 188 breast cancer specimens from 183 patients with measurable residual tumour.MethodsTumour size, ypT, and ypN categories were reassessed with the current yACC criteria and compared to the original pathology reports. Histological patterns of response in the breast were categorised as concentric or scattered.ResultsThe reassessed breast tumour size or ypT category differed from the original report in 101 (53.7%) cases. Changes in the ypT and/or ypN category resulted in downstaging of 45/185 (24.3%). A smaller reassessed tumour size or lower ypT category occurred more often in hormone receptor‐positive/HER2‐negative (HR+/HER2−) (68.3%) and HER2‐positive (HER2+) tumours (74.0%) than triple‐negative breast cancer (TNBC) (37.5%) (P < 0.001). A scattered pattern of response was more frequent in HR+/HER2− (54.9%) and HER2+ (66.0%) tumours than TNBC (35.7%) (P = 0.006). Changes in size, ypT, or multifocality based on the 8th Ed. yAJCC criteria were more frequent in tumours with a scattered pattern of response (P < 0.001).ConclusionStrict adherence to yAJCC criteria for measurement of the residual breast tumour after NAC resulted in smaller tumour size, lower ypT category, lower yAJCC stage, and more frequent classification of residual tumour as multifocal. Downstaging based on 8th Ed. yAJCC criteria was associated with tumour subtype and histological pattern of response.

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Comparison of Breast Cancer Staging Systems After Neoadjuvant Chemotherapy

Cited by 5 publications

References 23 publications

Development, verification, and comparison of a risk stratification model integrating residual cancer burden to predict individual prognosis in early-stage breast cancer treated with neoadjuvant therapy

Development, verification, and comparison of a risk stratification model integrating residual cancer burden to predict individual prognosis in early-stage breast cancer treated with neoadjuvant therapy

Evaluation of the relationship between Ki67 expression level and neoadjuvant treatment response and prognosis in breast cancer based on the Neo-Bioscore staging system

Pathological measurement and staging of residual breast cancer after neoadjuvant chemotherapy

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