Diagnostic accuracy of contrast-enhanced spectral mammography in assessing breast cancer response to neoadjuvant chemotherapy

Abstract Background Assessing tumor response to neoadjuvant chemotherapy (NAC) in breast cancer remains a clinical challenge. Traditional methods, such as the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) and Miller–Payne (MP) grading, have limitations in accurately reflecting the true pathological response. Contrast-enhanced spectral mammography (CESM) has recently emerged as a promising imaging tool that provides morphological and functional information. The updated combined CESM technique combines conventional quantitative size measurements with radiomics-based qualitative features derived from CESM, including enhancement intensity and pattern. The aim of this study was to evaluate the diagnostic accuracy of CESM (updated combined assessment) in predicting tumor response to NAC, compared with RECIST 1.1 and Miller–Payne histopathological grading. Results This prospective study included 52 female patients who met the inclusion criteria based on the available resources. The included patients had a biopsy-proven breast cancer and underwent CESM before and after NAC. Tumor response was assessed using RECIST 1.1 and updated combined CESM assessment, and correlated with the Miller–Payne classification as the reference standard. Statistical analysis included Kappa agreement, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. CESM updated combined assessment revealed nearly perfect agreement with Miller–Payne (95% CI, K = 0.96, p < 0.001), having sensitivity 100%, specificity 94.7%, PPV 97.1%, NPV 100%, and overall accuracy 98.1%. RECIST 1.1 also showed strong agreement (95% CI, K = 0.84, p < 0.001), with sensitivity 100%, specificity 81.8%, PPV 88.2%, NPV 100%, and accuracy 92.3%. CESM was particularly superior in detecting moderate and marked responses. Conclusions CESM provides a reliable, cost-effective, and accurate imaging tool for monitoring NAC response in breast cancer patients, thanks to its strong concordance with histopathological outcomes. CESM is faster than MRI and better tolerated by patients. MRI, however, remains the gold standard, particularly for high-risk screening, but suffers from higher costs and longer procedures.

Authors

Institutions

Publication Details

Journal
The Egyptian Journal of Radiology and Nuclear Medicine
Published
2026-06-27
DOI
https://doi.org/10.1186/s43055-026-01801-4
Primary Topic
Digital Radiography and Breast Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Diagnostic accuracy of contrast-enhanced spectral mammography in assessing breast cancer response to neoadjuvant chemotherapy

Khalid Mohammed Rizk, Ahmed M. AbdelHakam, Nagham Nabil Omar, Abolhasan Haseib Mohammad et al.
The Egyptian Journal of Radiology and Nuclear Medicine
Digital Radiography and Breast Imaging
article

Diagnostic accuracy of contrast-enhanced spectral mammography in assessing breast cancer response to neoadjuvant chemotherapy

Khalid Mohammed Rizk, Ahmed M. AbdelHakam, Nagham Nabil Omar, Abolhasan Haseib Mohammad, Ehab Mansour Mohammed, Amal Rayan Ibrahim Rayan, Doaa Mahmoud Ahmed Tamam
article en

Abstract

Abstract Background Assessing tumor response to neoadjuvant chemotherapy (NAC) in breast cancer remains a clinical challenge. Traditional methods, such as the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) and Miller–Payne (MP) grading, have limitations in accurately reflecting the true pathological response. Contrast-enhanced spectral mammography (CESM) has recently emerged as a promising imaging tool that provides morphological and functional information. The updated combined CESM technique combines conventional quantitative size measurements with radiomics-based qualitative features derived from CESM, including enhancement intensity and pattern. The aim of this study was to evaluate the diagnostic accuracy of CESM (updated combined assessment) in predicting tumor response to NAC, compared with RECIST 1.1 and Miller–Payne histopathological grading. Results This prospective study included 52 female patients who met the inclusion criteria based on the available resources. The included patients had a biopsy-proven breast cancer and underwent CESM before and after NAC. Tumor response was assessed using RECIST 1.1 and updated combined CESM assessment, and correlated with the Miller–Payne classification as the reference standard. Statistical analysis included Kappa agreement, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. CESM updated combined assessment revealed nearly perfect agreement with Miller–Payne (95% CI, K = 0.96, p < 0.001), having sensitivity 100%, specificity 94.7%, PPV 97.1%, NPV 100%, and overall accuracy 98.1%. RECIST 1.1 also showed strong agreement (95% CI, K = 0.84, p < 0.001), with sensitivity 100%, specificity 81.8%, PPV 88.2%, NPV 100%, and accuracy 92.3%. CESM was particularly superior in detecting moderate and marked responses. Conclusions CESM provides a reliable, cost-effective, and accurate imaging tool for monitoring NAC response in breast cancer patients, thanks to its strong concordance with histopathological outcomes. CESM is faster than MRI and better tolerated by patients. MRI, however, remains the gold standard, particularly for high-risk screening, but suffers from higher costs and longer procedures.

The Egyptian Journal of Radiology and Nuclear MedicineVol. 57(1)
Assiut University (EG)
Gender equality
Openalex Percentile: Top 7%
Digital Radiography and Breast Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.