Factors influencing agreement of breast cancer luminal molecular subtype by Ki67 labeling index between core needle biopsy and surgical resection specimens

Tendl-Schulz, Kristina; Wimmer, Philipp; Heber, Ulrike; Mittlböck, Martina; Kozakowski, Nicolas; Pinker, Katja; Bartsch, Rupert; Dubsky, Peter; Fitzal, Florian; Filipits, Martin; Eckel, Fanny Carolina; Langthaler, Eva-Maria; Steger, G.; Gnant, Michael; Singer, Christian F.; Helbich, Thomas H.; Bagó-Horváth, Zsuzsanna

doi:10.1007/s00428-020-02818-4

Cited by 6 publications

(5 citation statements)

References 46 publications

(64 reference statements)

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“…To date, few published articles have evaluated the prognostic value of Ki-67 change after CNB in untreated breast cancer patients ( 41 ). Previously, a small-sized study by Kristina A. Tendl-Schulz et al showed that agreement of Ki-67 level between biopsy and surgery did not significantly impact recurrence-free survival (RFS) or OS ( P = 0.22 for both) ( 41 ). Herein, we demonstrated in the large-scale cohort that Ki-67 change pattern after CNB was significantly associated with RFI and BCSS in HR+/HER2− breast cancers.…”

Section: Discussionmentioning

confidence: 99%

Association of Ki-67 Change Pattern After Core Needle Biopsy and Prognosis in HR+/HER2− Early Breast Cancer Patients

Chen

Shen

2022

Front. Surg.

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BackgroundTo investigate the association of Ki-67 change pattern after core needle biopsy (CNB) and prognosis in HR+/HER2− early breast cancer patients.MethodEligible patients were categorized into three groups: Low group, Elevation group, and High group. Chi-square test and logistic regression analysis were used to compare the clinic-pathological characteristics. Kaplan–Meier method was used to estimate the rates of recurrence-free interval (RFI) and breast cancer-specific survival (BCSS), which were compared via the Log-rank test. Cox proportional hazard analysis was performed to investigate independent prognostic factors.ResultsA total of 2,858 patients were included: 1,179 (41.3%), 482 (16.9%), and 1,197 (41.8%) patients were classified into the low, elevation, and high groups, respectively. Age, tumor size, histological grade, lymph-vascular invasion (LVI), and ER level status were associated with Ki-67 change pattern after CNB. With a median follow-up of 53.6 months, the estimated 5-year RFI rates for the low group, elevation, and high groups were 96.4%, 95.3% and 90.9%, respectively (P < 0.001). And 5-year BCSS rates were 99.3%, 98.3% and 96.8%, respectively (P = 0.001). Compared with patients in the low group, patients in the high group had significantly worse RFI (hazard ratio [HR] 1.71, 95% confidence interval [CI] 1.16–2.54) in multivariate analysis.ConclusionsKi-67 change after CNB was associated with prognosis in HR+/HER2− early breast cancer. Patients with Ki-67 high or elevation after CNB had an inferior disease outcome, indicating the necessity of re-evaluating Ki-67 on surgical specimens after CNB.

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Section: Discussionmentioning

confidence: 99%

Association of Ki-67 Change Pattern After Core Needle Biopsy and Prognosis in HR+/HER2− Early Breast Cancer Patients

Chen

Shen

2022

Front. Surg.

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“…Research has indicated that the Ki-67 expression levels are closely related to differentiation levels, infiltration, metastasis, and prognosis of rectal cancer and directly affect prognosis outcomes [ 9 , 10 ]. However, Ki-67 expression can only be determined through invasive biopsy or surgical pathology tissues [ 11 ]. Preoperative examination approaches assessing Ki-67 expression enabling to predict patient prognosis without having to undergo invasive examinations or surgery can thus be beneficial for patients.…”

Section: Introductionmentioning

confidence: 99%

“…stage: N0 (no positive lymph node), N1 (1-3 positive lymph node) or N2 (≥ 4 positive lymph node). It was considered positive if lymph node showed suspicious morphology (round shape, irregular border, heterogeneous signal) and short axis diameter > 9 mm[13];(11) enhancement pattern (EP): Mild/Moderate or obvious enhancement of tumor compared to normal rectal wall enhancement (Fig.2a,b); and (12) rM: presence of metastasis determined according to radiological examination. Discrepancies between the radiologists were resolved by consensus after joint re-evaluation of the images and confirmed by another radiologist with 20 years of experience in abdominal diagnosis.…”

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confidence: 99%

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

Yao,

Ao,

Zhu

et al. 2023

BMC Med Imaging

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Background To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. Methods Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. Results Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. Conclusion The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

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“…High Ki67 expression in PCa is a poor prognostic factor and is correlated with overall survival, disease-free survival, and distant metastasis [ 8 , 9 ]. The existing methods for detecting Ki67 expression levels are invasive, complex, barely reproducible, and susceptible to subjective influence [ 10 , 11 ]. Therefore, researchers must develop noninvasive, simple, and reproducible methods for detecting Ki67 expression in patients with PCa.…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

et al. 2023

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Background To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa). Materials The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient’s prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated. Results Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly. Conclusion The multiple easy-to-use deep learning–based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

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Factors influencing agreement of breast cancer luminal molecular subtype by Ki67 labeling index between core needle biopsy and surgical resection specimens

Cited by 6 publications

References 46 publications

Association of Ki-67 Change Pattern After Core Needle Biopsy and Prognosis in HR+/HER2− Early Breast Cancer Patients

Association of Ki-67 Change Pattern After Core Needle Biopsy and Prognosis in HR+/HER2− Early Breast Cancer Patients

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

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