Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy

Article Radiology, Nuclear Medicine & Medical Imaging

Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography

Ning Mao et al.

Summary: Radiomics based on intratumoral and peritumoral regions from CESM images showed potential in predicting the NAC effect in breast cancer and could assist in treatment decision-making.

EUROPEAN RADIOLOGY (2022)

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Article Oncology

Treatment Response Prediction Using Ultrasound-Based Pre-, Post-Early, and Delta Radiomics in Neoadjuvant Chemotherapy in Breast Cancer

Min Yang et al.

Summary: This study developed and validated a nomogram based on radiomics features and clinical characteristics for early prediction of response to neoadjuvant chemotherapy in breast cancer. The nomogram showed good performance in terms of predicting chemotherapy response, providing valuable information for individual treatment and adjustment of chemotherapy regimens.

FRONTIERS IN ONCOLOGY (2022)

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Article Oncology

Cancer Statistics, 2021

Rebecca L. Siegel et al.

Summary: Every year, the American Cancer Society projects the numbers of new cancer cases and deaths in the United States, with the latest data showing a significant decline in lung cancer mortality, while prostate cancer mortality has plateaued and breast and colorectal cancer mortality have slowed. Improvements in treatment have accelerated progress against lung cancer, leading to a record drop in overall cancer mortality.

CA-A CANCER JOURNAL FOR CLINICIANS (2021)

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Article Oncology

Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT

Xiaomei Huang et al.

Summary: This study retrospectively analyzed 215 patients and developed a radiomic nomogram for predicting pathologic complete response to neoadjuvant therapy in breast cancer. The nomogram, with an area under the curve of 0.818, showed promising performance in pretreatment individualized prediction of therapeutic effect and could aid in improving patient outcomes.

CLINICAL BREAST CANCER (2021)

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Article Multidisciplinary Sciences

Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy

K. Dobruch-Sobczak et al.

Summary: Multiparametric ultrasound was used to evaluate treatment response in breast cancer patients undergoing neoadjuvant chemotherapy. Significant correlations were found between tumor echogenicity, diameter, and residual malignant cell content.

SCIENTIFIC REPORTS (2021)

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Article Oncology

Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer

Meng Jiang et al.

Summary: The study developed and validated a deep learning radiomic nomogram for predicting pathological complete response in breast cancer patients after neoadjuvant chemotherapy using pre- and post-treatment ultrasound images. The model demonstrated high accuracy, good calibration, and outperformed clinical models and experts' predictions. The deep learning-based radiomic nomogram showed good predictive value and clinical usefulness for individualized treatment in locally advanced breast cancer.

EUROPEAN JOURNAL OF CANCER (2021)

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Article Oncology

A Practical Predictive Model Based on Ultrasound Imaging and Clinical Indices for Estimation of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Pingping Ye et al.

Summary: The study developed a predictive model using ultrasound imaging and clinical indices to identify breast cancer patients who are most likely to benefit from neoadjuvant chemotherapy (NACT). Multivariate logistic regression identified several significant factors associated with NACT clinical responses, and the model demonstrated robust performance in both training and validation cohorts.

CANCER MANAGEMENT AND RESEARCH (2021)

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Article Oncology

MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study

Renee W. Y. Granzier et al.

Summary: This study investigated the use of pretreatment contrast-enhanced MRI-based radiomics for predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. The radiomics features did not provide additional value compared to clinical models, indicating the need for reproducibility studies to select reproducible features for accurate assessment of radiomics potential.

CANCERS (2021)

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Article Oncology

3T DCE-MRI Radiomics Improves Predictive Models of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Stefania Montemezzi et al.

Summary: Radiomic features extracted from 3T DCE-MRI consistently improved the performance of predictive models of complete response to neo-adjuvant chemotherapy for breast malignant lesions. The inclusion of radiomic information resulted in AUC values higher than 0.90 across different classifiers, indicating its potential for improving clinical decision making. Further investigation is needed to validate these findings.

FRONTIERS IN ONCOLOGY (2021)

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Correction Multidisciplinary Sciences

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer (vol 11, 1236, 2021)

Xueyi Zheng et al.

NATURE COMMUNICATIONS (2021)

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Article Medicine, General & Internal

Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study

Yunfang Yu et al.

Summary: Machine learning techniques were used to develop an efficient preoperative MRI radiomics evaluation approach for predicting ALN status in early-stage invasive breast cancer patients. The multiomic signature showed a high prediction quality and potential for tailored surgical management.

EBIOMEDICINE (2021)

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Article Oncology

Radiomics of MRI for the Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Single Referral Centre Analysis

Filippo Pesapane et al.

Summary: The study aimed to predict the response to neoadjuvant chemotherapy in breast cancer patients using radiomic features extracted from MRI, in addition to biological characteristics. The combination of radiomics and clinical/biological models slightly improved the prediction of pathological complete responses, potentially helping to avoid unnecessary treatment.

CANCERS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging