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A nomogram based on clinicopathological features and serological indicators predicting breast pathologic complete response of neoadjuvant chemotherapy in breast cancer.

ABSTRACT: A single tumor marker is not enough to predict the breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. We aimed to establish a nomogram based on multiple clinicopathological features and routine serological indicators to predict bpCR after NAC in breast cancer patients. Data on clinical factors and laboratory indices of 130 breast cancer patients who underwent NAC and surgery in First Affiliated Hospital of Xi'an Jiaotong University from July 2017 to July 2019 were collected. Multivariable logistic regression analysis identified 11 independent indicators: body mass index, carbohydrate antigen 125, total protein, blood urea nitrogen, cystatin C, serum potassium, serum phosphorus, platelet distribution width, activated partial thromboplastin time, thrombin time, and hepatitis B surface antibodies. The nomogram was established based on these indicators. The 1000 bootstrap resampling internal verification calibration curve and the GiViTI calibration belt showed that the model was well calibrated. The Brier score of 0.095 indicated that the nomogram had a high accuracy. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was 0.941 (95% confidence interval: 0.900-0.982) showed good discrimination of the model. In conclusion, this nomogram showed high accuracy and specificity and did not increase the economic burden of patients, thereby having a high clinical application value.

SUBMITTER: Li Y

PROVIDER: S-EPMC8167133 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram.

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| S-EPMC8650158 | biostudies-literature

Multivariable Models Based on Baseline Imaging Features and Clinicopathological Characteristics to Predict Breast Pathologic Response after Neoadjuvant Chemotherapy in Patients with Breast Cancer.

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| S-EPMC9247529 | biostudies-literature

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| S-EPMC9732273 | biostudies-literature

Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI.

Project description:BackgroundThis study aims to observe and analyze the effect of diffusion weighted magnetic resonance imaging (MRI) on the patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy.MethodsFifty patients (mean age, 48.7 years) with stage II-III breast cancer who underwent neoadjuvant chemotherapy and preoperative MRI between 2016 and 2020 were retrospectively evaluated. The associations between preoperative breast MRI findings/clinicopathological features and outcomes of neoadjuvant chemotherapy were assessed.ResultsClinical stage at baseline (OR: 0.104, 95% confidence interval (CI) 0.021-0.516, P = 0.006) and standard apparent diffusion coefficient (ADC) change (OR: 9.865, 95% CI 1.024-95.021, P = 0.048) were significant predictive factors of the effects of neoadjuvant chemotherapy. The percentage increase of standard ADC value in pathologic complete response (pCR) group was larger than that in non-pCR group at first time point (P < 0.05). A correlation was observed between the change in standard ADC values and tumor diameter at first follow-up (r: 0.438, P < 0.05).ConclusionsOur findings support that change in standard ADC values and clinical stage at baseline can predict the effects of neoadjuvant chemotherapy for patients with breast cancer in early stage.

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A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer

Project description: Not available

| S-EPMC10899694 | biostudies-literature

Assessment of Cone-Beam Breast Computed Tomography for Predicting Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer: A Prospective Study.

Project description:BackgroundResponse surveillance of neoadjuvant chemotherapy is needed to facilitate treatment decisions. We aimed to assess the imaging features of cone-beam breast computed tomography (CBBCT) for predicting the pathologic response of breast cancer after neoadjuvant chemotherapy.MethodsThis prospective study included 81 women with locally advanced breast cancer who underwent neoadjuvant chemotherapy from August 2017 to January 2021. All patients underwent CBBCT before treatment, and 55 and 65 patients underwent CT examinations during the midtreatment (3 cycles) and late-treatment phases (7 cycles), respectively. Clinical information and quantitative parameters such as the diameter, volume, surface area, and CT density were compared between pathologic responders and nonresponders using the T-test and the Mann-Whitney U test. The performance of meaningful parameters was evaluated with the receiver operating characteristic curve, sensitivity, and specificity.ResultsThe quantitative results for the segmented volume, segmented surface area, segmented volume reduction, maximum enhancement ratio, wash-in rate and two-minute enhancement value in the mid- and late-treatment periods had predictive value for pathologic complete response. The area under the curve for the prediction model after multivariate regression analysis was 0.874.ConclusionAfter comparing the outcomes of each timepoint, mid- and late-treatment parameters can be used to predict pathologic outcome. The late-treatment parameters showed significant value with a predictive model.

| S-EPMC9076291 | biostudies-literature

Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy.

Project description:BackgroundGiven the low chance of response to neoadjuvant chemotherapy (NACT) in luminal breast cancer (LBC), the identification of predictive factors of pathological complete response (pCR) represents a challenge. A multicenter retrospective analysis was performed to develop and validate a predictive nomogram for pCR, based on pre-treatment clinicopathological features.MethodsClinicopathological data from stage I-III LBC patients undergone NACT and surgery were retrospectively collected. Descriptive statistics was adopted. A multivariate model was used to identify independent predictors of pCR. The obtained log-odds ratios (ORs) were adopted to derive weighting factors for the predictive nomogram. The receiver operating characteristic analysis was applied to determine the nomogram accuracy. The model was internally and externally validated.ResultsIn the training set, data from 539 patients were gathered: pCR rate was 11.3% [95% confidence interval (CI): 8.6-13.9] (luminal A-like: 5.3%, 95% CI: 1.5-9.1, and luminal B-like: 13.1%, 95% CI: 9.8-13.4). The optimal Ki67 cutoff to predict pCR was 44% (area under the curve (AUC): 0.69; p < 0.001). Clinical stage I-II (OR: 3.67, 95% CI: 1.75-7.71, p = 0.001), Ki67 ⩾44% (OR: 3.00, 95% CI: 1.59-5.65, p = 0.001), and progesterone receptor (PR) <1% (OR: 2.49, 95% CI: 1.15-5.38, p = 0.019) were independent predictors of pCR, with high replication rates at internal validation (100%, 98%, and 87%, respectively). According to the nomogram, the probability of pCR ranged from 3.4% for clinical stage III, PR > 1%, and Ki67 <44% to 53.3% for clinical stage I-II, PR < 1%, and Ki67 ⩾44% (accuracy: AUC, 0.73; p < 0.0001). In the validation set (248 patients), the predictive performance of the model was confirmed (AUC: 0.7; p < 0.0001).ConclusionThe combination of commonly available clinicopathological pre-NACT factors allows to develop a nomogram which appears to reliably predict pCR in LBC.

| S-EPMC10017935 | biostudies-literature

Predicting pathologic response to neoadjuvant chemotherapy in breast cancer by using MR imaging and quantitative 1H MR spectroscopy.

Project description:PurposeTo compare changes in the concentration of choline-containing compounds (tCho) and in tumor size at follow-up after neoadjuvant chemotherapy (NAC) between patients who achieved pathologic complete response (pCR) and those who did not (non-pCR).Materials and methodsThis study was approved by the institutional review board and was compliant with HIPAA; each patient gave informed consent. Thirty-five patients (mean age, 48 years +/- 11 [standard deviation]; range, 29-75 years) with breast cancer were included. Treatment included doxorubicin and cyclophosphamide followed by a taxane-based regimen. Changes in tCho and tumor size in pCR versus non-pCR groups were compared by using the two-way Mann-Whitney nonparametric test. Receiver operating characteristic (ROC) analysis was performed to differentiate between them and the area under the ROC curve (AUC) was compared.ResultsIn the pCR group, the tCho level change was greater compared with change in tumor size (P = .003 at first follow-up, P = .01 at second follow-up), but they were not significantly different in the non-pCR group. Changes in tumor size and tCho level at the first follow-up study were not significantly different between the pCR and non-pCR groups but reached significance at the second follow-up. In ROC analysis, the magnetic resonance (MR) imaging and MR spectroscopic parameters had AUCs of 0.65-0.68 at first follow-up; at second follow-up, AUC for change in tumor size was 0.9, AUC for change in tCho was 0.73.ConclusionPatients who show greater reduction in tCho compared with changes in tumor size are more likely to achieve pCR. The change in tumor size halfway through therapy was the most accurate predictor of pCR.

| S-EPMC2687529 | biostudies-literature

Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies.

Project description:In clinical breast cancer intervention, selection of the optimal treatment protocol based on predictive biomarkers remains an elusive goal. Here, we present a modeling tool to predict the likelihood of breast cancer response to neoadjuvant chemotherapy using patient specific tumor vasculature biomarkers. A semi-automated analysis was implemented and performed on 3990 histological images from 48 patients, with 10-208 images analyzed for each patient. We applied a histology-based model to resected primary breast cancer tumors (n = 30), and then evaluated a cohort of patients (n = 18) undergoing neoadjuvant chemotherapy, collecting pre- and post-treatment pathology specimens and MRI data. We found that core biopsy samples can be used with acceptable accuracy (r = 0.76) to determine histological parameters representative of the whole tissue region. Analysis of model histology parameters obtained from tumor vasculature measurements, specifically diffusion distance divided by radius of drug source (L/rb) and blood volume fraction (BVF), provides a statistically significant separation of patients obtaining a pathologic complete response (pCR) from those that do not (Student's t-test; P < 0.05). With this model, it is feasible to evaluate primary breast tumor vasculature biomarkers in a patient specific manner, thereby allowing a precision approach to breast cancer treatment.

| S-EPMC6538356 | biostudies-literature

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