Dataset Information

A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors.

ABSTRACT: ki-67 score is a solid tumor proliferation marker being associated with the prognosis of breast carcinoma and its response to neoadjuvant chemotherapy. In the present study, we aimed to investigate the way of clustering of prognostic factors by ki-67 score using a machine learning approach and multiple correspondence analysis. In this study, 223 patients with breast carcinoma were analyzed using the random forest method for classification of prognostic factors according to ki-67 groups (<14% and >14%). Also the relationship between subgroups of prognostic factors and ki-67 scores was examined by multiple correspondence analysis. There was a clustering of molecular classification LA, 0-3 metastatic lymph node, age <50, absence of LVI, T1 tumor size with ki-67 <14% and grade III, 10 or more metastatic lymph nodes, and presence of LVI and molecular classification LB, age >50, and T3-T4 tumor size categories with ki-67 >14%. The fact that the low scores of ki-67 correlate with early stage diseases and high scores with advanced disease suggests that 14% threshold value is crucial for ki-67 score.

SUBMITTER: Dirican E

PROVIDER: S-EPMC6106968 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors.

Dirican E E Kiliç E E

Journal of oncology 20180807

ki-67 score is a solid tumor proliferation marker being associated with the prognosis of breast carcinoma and its response to neoadjuvant chemotherapy. In the present study, we aimed to investigate the way of clustering of prognostic factors by ki-67 score using a machine learning approach and multiple correspondence analysis. In this study, 223 patients with breast carcinoma were analyzed using the random forest method for classification of prognostic factors according to ki-67 groups (<14% and ...[more]

PMID: 30158977

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Project description:IntroductionKi-67 labeling index assessed by immunohistochemical assays has been shown useful in assessing the risk of recurrence for estrogen receptor (ER)-positive HER2-negative breast cancers (BC) and distinguishing Luminal A-like from Luminal B-like tumors. We aimed to assess the performance of the Ventana CONFIRM anti-Ki-67 (30-9) Rabbit Monoclonal Primary Antibody.MethodsWe constructed a case-cohort design based on a random sample (n?=?679) of all patients operated on for a first primary, non-metastatic, ER-positive, HER2-negative BC at the European Institute of Oncology (IEO) Milan, Italy during 1998-2002 and all additional patients (n?=?303) operated during the same period, who developed an event (metastasis in distant organs or death due to BC as primary event) and were not included in the previous subset. Multivariable Cox proportional hazards regression with inverse subcohort sampling probability weighting was used to evaluate the risk of event according to Ki-67 (30-9) and derived intrinsic molecular subtype, using previously defined cutoff values, i.e., respectively 14% and 20%.ResultsKi-67 was?<?14% in 318 patients (32.4%), comprised between 14 and 19% in 245 patients (24.9%) and???20 in 419 patients (42.7%). At multivariable analysis, the risk of developing distant disease was 1.88 (95% CI 1.20-2.93; P?=?0.006) for those with Ki-67 comprised between 14 and 19%, and 3.06 (95% CI 1.93-4.84; P?<?0.0001) for those with Ki-67???20% compared to those with Ki-67?<?14%. Patients with Luminal B-like BC had an approximate twofold risk of developing distant disease (HR?=?1.91; 95% CI 1.35-2.71; P?=?0.0003) than patients with Luminal A-like BC defined using Ki-67 (30-9).ConclusionsKi-67 evaluation using the 30-9 rabbit monoclonal primary antibody was able to stratify patients with ER-positive HER2-negative BC into prognostically distinct groups. Ki-67 assessment, with strict adherence to the international recommendations, should be included among the clinically useful biological parameters for the best treatment of patients with BC.

Dataset Information

A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors.

Publications

A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors.

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