Abstract P6-09-06: Updating the AJCC TNM staging system a summary of changes

Hortobagyi, G. N.; Giuliano, Antônio; Winchester, DJ; Mittendorf, EA; Edge, S.B.; Connolly, John A.; Weaver, Donald W.; Rugo, HS; Solin, Lawrence J.

doi:10.1158/1538-7445.sabcs16-p6-09-06

Cited by 12 publications

(14 citation statements)

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“…Accurate ways to predict the extent of invisible metastatic disease at diagnosis and risk of future metastatic relapse could help to personalize perioperative therapy protocols and avoid highly toxic therapies to patients with low risk of relapse [ 45 ]. However, only two risk models [ 47 , 48 ] have met the AJCC criteria for prognostic tools quality so far [ 49 ]. Both rely on classical Cox regression survival models.…”

Section: Discussionmentioning

confidence: 99%

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Benzekry,

Mastri,

Nicolò

et al. 2024

PLoS Comput Biol

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Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (i.e., circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (i.e., CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

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Section: Discussionmentioning

confidence: 99%

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Benzekry,

Mastri,

Nicolò

et al. 2024

PLoS Comput Biol

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“…22 Moreover, AJCC manual defined clinically suspicious nodes as having palpability on physical examination or suspicious imaging features. 21 Considering clinically ongoing trials such as SOUND, INSEMA, and BOOG 2013-08, the role of negative AUS in triaging omission of SLNB has become more important. In our study, the FNR of AUS was 19.2% and 20.1%, which were slightly lower than the 23.4–25% of previous studies.…”

Section: Discussionmentioning

confidence: 99%

“…Based on BI-RADS, positive axilla was considered when a LN with at least one of the following features was found: eccentric cortical thickening, diffuse cortical thickening of 3 mm or large, rounded hypoechoic, complete or partial effacement of the fatty hilum, and nonhilar cortical blood flow on color Doppler images. 21 23 If the assessment for axillary LN were equivocal or borderline, two breast radiologists (K.R.C., S.E.S. with 21 and 11 years of experience in breast imaging, respectively) re-evaluated the axillary LN status and categorized it into negative or positive by consensus.…”

Section: Methodsmentioning

confidence: 99%

“…According to the eighth edition of the American Joint Committee on Cancer (AJCC) staging, pN1mi which means micrometastases (larger than 0.2 mm, but none larger than 2.0 mm) at LN was defined as pN1. 21 At least one LN metastasis including pN1mi was considered as disease positive. In addition, HNB was defined as ≥ 3 positive LNs and low nodal burden was defined as 1–2 positive LNs according to the Z0011 trial.…”

Section: Methodsmentioning

confidence: 99%

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Value of Breast MRI and Nomogram After Negative Axillary Ultrasound for Predicting Axillary Lymph Node Metastasis in Patients With Clinically T1-2 N0 Breast Cancer

Song

Cho

et al. 2023

J Korean Med Sci

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Background There are increasing concerns about that sentinel lymph node biopsy (SLNB) could be omitted in patients with clinically T1-2 N0 breast cancers who has negative axillary ultrasound (AUS). This study aims to assess the false negative result (FNR) of AUS, the rate of high nodal burden (HNB) in clinically T1-2 N0 breast cancer patients, and the diagnostic performance of breast magnetic resonance imaging (MRI) and nomogram. Methods We identified 948 consecutive patients with clinically T1-2 N0 cancers who had negative AUS, subsequent MRI, and breast conserving therapy between 2013 and 2020 from two tertiary medical centers. Patients from two centers were assigned to development and validation sets, respectively. Among 948 patients, 402 (mean age ± standard deviation, 57.61 ± 11.58) were within development cohort and 546 (54.43 ± 10.02) within validation cohort. Using logistic regression analyses, clinical-imaging factors associated with lymph node (LN) metastasis were analyzed in the development set from which nomogram was created. The performance of MRI and nomogram was assessed. HNB was defined as ≥ 3 positive LNs. Results The FNR of AUS was 20.1% (81 of 402) and 19.2% (105 of 546) and the rates of HNB were 1.2% (5/402) and 2.2% (12/546), respectively. Clinical and imaging features associated with LN metastasis were progesterone receptor positivity, outer tumor location on mammography, breast imaging reporting and data system category 5 assessment of cancer on ultrasound, and positive axilla on MRI. In validation cohorts, the positive predictive value (PPV) and negative predictive value (NPV) of MRI and clinical-imaging nomogram was 58.5% and 86.5%, and 56.0% and 82.0%, respectively. Conclusion The FNR of AUS was approximately 20% but the rate of HNB was low. The diagnostic performance of MRI was not satisfactory with low PPV but MRI had merit in reaffirming negative AUS with high NPV. Patients who had low probability scores from our clinical-imaging nomogram might be possible candidates for the omission of SLNB.

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“…This new approach, observing distinct characteristics of the molecular subtypes concerning age, ethnicity, radiological presentation, response to therapy, and distant metastasis type, has identified patients who are candidates for specific therapies with positive effects on survival rates 12 , 13 , 14 . Although the molecular analysis of the risk of recurrence in some molecular subtypes is included in the latest American Joint Committee on Cancer (AJCC) guidelines, the molecular subtype still needs to be incorporated into the TNM staging approach 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

Molecular subtypes as a prognostic breast cancer factor in women users of the São Paulo public health system, Brazil

Peres¹,

Arantes

Fagundes

et al. 2023

Rev. bras. epidemiol.

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Objective: This study aimed to analyze the prognosis of women with breast cancer by molecular subtypes, sociodemographic variables, and clinical and treatment characteristics. Methods: This hospital-based retrospective cohort study analyzed 1,654 women over 18 years of age diagnosed with invasive breast cancer from 2000 to 2018. Data were extracted from Brazil’s Oncocenter Foundation of São Paulo. The variables analyzed were age, histology, molecular subtypes, clinical staging, treatment type, and diagnosis-to-treatment time. Cox regression analysis was applied to estimate death risk. Results: Women with HER-2-positive (nonluminal) and triple-negative molecular subtypes were more than twice more likely to be at risk of death, with adjusted hazard ratio — HRadj=2.30 (95% confidence interval — 95%CI 1.34–3.94) and HRadj=2.51 (95%CI 1.61–3.92), respectively. A delayed treatment associated with an advanced clinical stage at diagnosis increased fourfold the risk of death (HRadj=4.20 (95%CI 2.36–7.49). Conclusion: In summary, besides that interaction between advanced clinical stage and longer time between diagnosis and treatment, HER-2-positive (nonluminal) and triple-negative phenotypes were associated with a worse prognosis. Therefore, actions to reduce barriers in diagnosis and treatment can provide better outcome, even in aggressive phenotypes.

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Abstract P6-09-06: Updating the AJCC TNM staging system a summary of changes

Abstract: This abstract was withdrawn by the authors.

Cited by 12 publications

References 0 publications

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Value of Breast MRI and Nomogram After Negative Axillary Ultrasound for Predicting Axillary Lymph Node Metastasis in Patients With Clinically T1-2 N0 Breast Cancer

Molecular subtypes as a prognostic breast cancer factor in women users of the São Paulo public health system, Brazil

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