Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study

2021 ◽  
pp. 20210188
Author(s):  
Feihong Yu ◽  
Jing Hang ◽  
Jing Deng ◽  
Bin Yang ◽  
Jianxiang Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Validation Cohort ◽  
External Validation ◽  
Disease Free Survival ◽  
Internal Validation ◽  
Free Survival ◽  
Clinicopathological Variables ◽  
Disease Free ◽  
Radiomics Signature

Objectives: To explore the predictive value of radiomics nomogram using pretreatment ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer (TNBC). Methods and materials: A total of 486 TNBC patients from 3 different institutions were consecutively recruited for this study. They were categorized into the primary cohort (n = 216), as well as the internal validation cohort (n = 108) and external validation cohort (n = 162). In primary cohort, least absolute shrinkage and selection operator logistic regression algorithm was used to select recurrence-related radiomics features extracted from the breast tumor and peritumor regions, and a radiomics signature was constructed derived from the grayscale ultrasound images. A radiomic nomogram integrating independent clinicopathological variables and radiomic signature was established with uni- and multivariate cox regressions. The predictive nomogram was validated using an internal cohort and an independent external cohort regarding abilities of discrimination, calibration and clinical usefulness. Results: The patients with higher Rad-score had a worse prognostic outcome than those with lower Rad-score in primary cohort and two validation cohorts (All p < 0.05).The radiomics nomogram indicated more effective prognostic performance compared with the clinicopathological model and tumor node metastasis staging system (p < 0.01), with a training C-index of 0.75 (95% confidence interval (CI), 0.71–0.80), an internal validation C-index of 0.73 (95% CI, 0.69–0.78) and an external validation 0.71 (95% CI,0.66–0.76). Moreover, the calibration curves revealed a good consistency for survival prediction of the radiomics model. Conclusions: The ultrasound-based radiomics signature was a promising biomarker for risk stratification for TNBC patients. Furthermore, the proposed radiomics modal integrating the optimal radiomics features and clinical data provided individual relapse risk accurately. Advances in knowledge: The radiomics model integrating radiomic signature and independent clinicopathological variables could improve individual prognostic evaluation and facilitate therapeutic decision-making, which demonstrated the incremental value of the radiomics signature for prognostic prediction in TNBC.

scholarly journals Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Lang Xiong ◽  
Haolin Chen ◽  
Xiaofeng Tang ◽  
Biyun Chen ◽  
Xinhua Jiang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Cox Regression ◽  
Validation Cohort ◽  
Disease Free Survival ◽  
Free Survival ◽  
Net Reclassification Improvement ◽  
Additional Value ◽  
Disease Free ◽  
Radiomics Signature

BackgroundAccurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to predict disease-free survival (DFS) in patients with invasive breast cancer and assess its additional value to the clinicopathological predictors for individualized DFS prediction.MethodsWe identified 620 patients with invasive breast cancer and randomly divided them into the training (n = 372) and validation (n = 248) cohorts. A radiomics signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in the training cohort and validated in the validation cohort. Univariate and multivariate Cox proportional hazards model and Kaplan–Meier survival analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. To evaluate the additional value of the radiomics signature for DFS prediction, a radiomics nomogram combining the radiomics signature and clinicopathological predictors was constructed and assessed in terms of discrimination, calibration, reclassification, and clinical usefulness.ResultsThe radiomics signature was significantly associated with DFS, independent of the clinicopathological predictors. The radiomics nomogram performed better than the clinicopathological nomogram (C-index, 0.796 vs. 0.761) and provided better calibration and positive net reclassification improvement (0.147, P = 0.035) in the validation cohort. Decision curve analysis also demonstrated that the radiomics nomogram was clinically useful.ConclusionUS radiomics signature is a potential imaging biomarker for risk stratification of DFS in invasive breast cancer, and US-based radiomics nomogram improved accuracy of DFS prediction.

scholarly journals A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Bingqing Xia ◽  
He Wang ◽  
Zhe Wang ◽  
Zhaoxia Qian ◽  
Qin Xiao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Disease Free Survival ◽  
Training Group ◽  
Validation Group ◽  
Mri Findings ◽  
Free Survival ◽  
Clinicopathological Variables ◽  
Disease Free

Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS.Methods: Patients (n = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital (n = 109) were used as the training group, and patients from the other hospital (n = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation.Results: The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (p&lt;0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761–0.907) and 0.868 (95% CI, 0.787–949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709–0.743; validation group: C-index = 0.774,95% CI = 0.743–0.805).Conclusion: The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation.

Development of prognostic nomograms using institutional data for patients with triple-negative breast cancer

2021 ◽  
Author(s):  
Jie-Yu Zhou ◽  
Kang-Kang Lu ◽  
Wei-Da Fu ◽  
Hao Shi ◽  
Jun-Wei Gu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Disease Free Survival ◽  
Discriminative Ability ◽  
Free Survival ◽  
Disease Free

Background: Triple-negative breast cancer (TNBC) is an aggressive disease. Nomograms can predict prognosis of patients with TNBC. Methods: A total of 745 eligible TNBC patients were recruited and randomly divided into training and validation groups. Endpoints were disease-free survival and overall survival. Concordance index, area under the curve and calibration curves were used to analyze the predictive accuracy and discriminative ability of nomograms. Results: Based on the training cohort, neutrophil-to-lymphocyte ratio, positive lymph nodes, tumor size and tumor-infiltrating lymphocytes were used to construct a nomogram for disease-free survival. In addition, age was added to the overall survival nomogram. Conclusion: The current study developed and validated well-calibrated nomograms for predicting disease-free survival and overall survival in patients with TNBC.

scholarly journals AKAP3 correlates with triple negative status and disease free survival in breast cancer

BMC Cancer ◽  
2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Rezvan Esmaeili ◽  
Keivan Majidzadeh-A ◽  
Leila Farahmand ◽  
Maryam Ghasemi ◽  
Malihe Salehi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Disease Free Survival ◽  
Free Survival ◽  
Disease Free

Different effects of insulin-like growth factor-1 receptor expression on prognosis of estrogen receptor positive versus triple-negative invasive ductal breast carcinoma

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 3546-3546
Author(s):  
J. Wesseling ◽  
H. Hartog ◽  
H. Horlings ◽  
B. van der Vegt ◽  
A. Ajouaou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Growth Factor ◽  
Triple Negative ◽  
Systemic Treatment ◽  
Disease Free Survival ◽  
P Value ◽  
Invasive Ductal Breast Carcinoma ◽  
Free Survival ◽  
Disease Free

3546 Background: The insulin-like growth factor type 1 receptor (IGF-1R) is involved in progression and sensitivity to systemic treatment of breast cancer. Moreover, targeted inhibition of IGF-1R is likely to be beneficial in systemic treatment. However, it is unknown how to select patients for IGF-1R targeted therapy. Therefore, we studied the relation between IGF-1R expression and prognosis in invasive ductal breast carcinomas. Methods: Immunohistochemistry was performed on tumor tissue of a consecutive cohort of 429 female patients treated for operable primary invasive ductal breast carcinoma. TMA sections were stained with antibodies against IGF1-R, insulin receptor (IR), ER, PR, HER-2, epidermal growth factor receptor (EGFR) and phosphorylated-Akt (p-Akt). Cytoplasmic and membranous IGF-1R staining were scored separately, as the relevance of IGF-1R cellular localization is yet unknown. Associations between IGF-1R expression with clinical and tumor characteristics were evaluated in a multivariate Cox regression model. To study in more detail the prognostic role of IGF-1R expression in triple negative invasive ductal carcinomas (TN IDCs), 51 TN IDCs from the series described above were combined with 64 TN IDCs from an independent dataset with similar patient and clinico-pathological characteristics. Results: Patients with tumors expressing both ER and cytoplasmic IGF-1R have a longer disease free survival (HR = 0.20; 95% CI 0.07 - 0.63; p-value = 0.006) and breast cancer specific survival (HR = 0.20, 95% CI 0.07 - 0.63, p-value = 0.002), independent of other known prognostic factors. Conversely, in the combined series of 105 TN IDCs, cytoplasmic IGF-1R expression was associated with a shorter disease free survival (HR = 2.29; 95% CI 1.08 - 4.48, p-value = 0.03). In a multivariate model including known prognostic factors, cytoplasmic IGF-1R expression was nearly significantly related to a shorter disease free survival (HR 2.06; 95% CI 0.95 - 4.47; p = 0.07). Conclusions: The favorable versus unfavorable association with prognosis of IGF-1R expression in ER positive versus TN IDCs may provide new opportunities to select patients for IGF-1R targeted therapy. No significant financial relationships to disclose.

The prognostic importance of triple negative breast cancer: A population based study in India

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22219-e22219
Author(s):  
B. S. Ajaikumar ◽  
R. Rao ◽  
J. Prabhu ◽  
J. D. Kulkarni ◽  
P. K ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Triple Negative ◽  
Disease Free Survival ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Node Status ◽  
Disease Free

e22219 Background: Triple-negative (ER-negative, PR-negative, HER2/neu negative) breast cancer has distinct clinical and pathologic features, and is a clinical problem because of its typically high grade, relatively poor prognosis, aggressive behavior and lack of targeted therapies leaving chemotherapy as the mainstay of treatment. This study envisaged to analyse the influence of triple negativity status on survival and disease free survival in prospective cohort of breast cancer patients. Methods: Breast tumors of 215 women aged 30–75, diagnosed from 2004 were tested for ER, PR and HER2 positivity by immunohistochemistry and correlated with clinical outcomes such as recurrence, disease free survival and overall survival using Kaplan Meiers Survival analysis and Coxs regression analysis. The study cohort was followed up for 60 months or until death whichever was earlier. Results: Triple negativity significantly influenced disease free survival (46 ± 3, 41, 52) vs. non triple negative cohort (mean ± SE; 95%CI, 37 ± 2; 32, 40) and log rank = 2.1, p = 0.04. However triple negativity did not influence overall survival in months (56 ± 0; 55, 56) vs. non triple negative cohort (43 ± 1; 42, 45), (log rank = 1.78, p = 0.16). However, the mean disease free survival was (45 ± 7; 32, 58) months for patients >40 years age vs (37 ± 4; 33, 39) for patients < 40 years of age (log rank = 2.87, p =0.02). Stage of disease, node status, grade and menopausal status did not influence disease free survival significantly. However, Cox regression analysis did not predict significant effects of triple negativity on overall survival or disease free survival when controlled for confounding factors such as age, node status, stage etc Conclusions: Our observations suggest that triple negativity can significantly affect progression of breast cancer in Indian breast cancer patients and longer follow up is necessary (10 years) to determine its effects on survival. No significant financial relationships to disclose.

Clinical impact of 18F-FDG–PET in breast cancer: Survival analysis and comparison of intrinsic subtypes with standardized uptake value.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e12005-e12005
Author(s):  
Sung Gwe Ahn ◽  
Hak Min Lee ◽  
Seung Ah Lee ◽  
Tae Joo Jeon ◽  
Young Hoon Yoo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Fdg Pet ◽  
Triple Negative ◽  
Disease Free Survival ◽  
Clinical Impact ◽  
Free Survival ◽  
18F Fdg ◽  
Validation Set ◽  
Disease Free

e12005 Background: [18F] fluorodeoxyglucose positron emission tomography (18F-FDG–PET) scans are known as an important imaging study with their reflection biological activity in various malignancies. To investigate clinical impact of 18F-FDG–PET in breast cancer, we performed survival analysis with maximum standardized uptake values (SUVmax), and compared SUVmax according to breast cancer subtypes. Methods: We reviewed the medical records of 462 patients with breast cancer who underwent primary surgery between April 2004 and December 2008 at single institute. Patients were classified as 4 subtypes: luminal A, luminal B, HER2 and triple negative. The entire patients were randomly assigned as training set (n=220) and validation set (n=242). Results: High SUVmax vs. low SUVmax group was defined with cut-off points of 4 in a training set.At a median follow-up of 6.03 years, the patients with high SUVmax had a shorter disease-free survival in a validation set (p = 0.018, log-rank test). Using multivariate analysis for disease-free survival in the entire patients, high SUVmax was demonstrated as a poor prognostic factor (hazard ratio 2.34, 95% confidence interval 1.18-4.67). In the comparison among subtypes, mean of SUVmax was higher in triple negative type and HER2 type. Also, high SUVmax was significantly associated with larger tumor size, positive node, high Ki67 (≥14%), ER negative, and high histologic grade, and HER2 positive. Conclusions: In breast cancer, the patients with higher SUVmax showed a shorter disease-free survival. Higher SUVmax found in HER-2 and triple negative type suggests that 18F-FDG PET could reflect molecular signature.

Sign in / Sign up

Export Citation Format

Share Document