Assessment of CAD-generated tumor volumes measured using MRI in breast cancers before and after neoadjuvant chemotherapy

Kinetic Heterogeneity Improves the Specificity of Dynamic Enhanced MRI in Differentiating Benign and Malignant Breast Tumours

RATIONALE AND OBJECTIVES

To investigate whether kinetic heterogeneity in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) improves the specificity of breast cancer (BC) diagnosis.

MATERIALS AND METHODS

The DCE-MRI data of patients with benign breast tumours and BC from June 2020 to July 2022 were retrospectively evaluated. MATLAB and SPM were used to determine six major kinetic parameters: peak, enhancement volume, heterogeneity, as well as persistent, plateau, and washout proportions. Continuous variables were compared using the Student's t-test or Mann-Whitney U tests, and categorical variables were compared using the chi-square or Fisher's exact tests. Receiver operating characteristic curves were plotted. The intraclass correlation coefficient (ICC) was used to evaluate agreement between the two observers. Multivariate logistic regression analysis was conducted to calculate the odds ratios (ORs) with 95% confidence intervals (CIs) for the association between benign and malignant breast tumours.

RESULTS

In total, 147 patients (mean age, 47 years old) were included in the study, 76 of whom had BC. Data analysis by the two observers showed good consistency in the peak, enhancement volume, persistent proportion, plateau proportion, washout proportion, and heterogeneity, with ICCs of 0.865, 0.988, 0.906, 0.940, 0.740, and 0.867, respectively (p < 0.001). In the DCE kinetic analysis, differences in all the six kinetic parameters were statistically significant (p < 0.05). The area under the curve for heterogeneity was 0.92 (95% CI:0.88,0.97), and the sensitivity and specificity were 0.895 and 0.845, respectively. Multivariate logistic regression analysis showed that heterogeneity was an independent predictor of BC compared to benign breast tumours (OR=2.020; 95% CI:1.316, 3.100; p = 0.001).

CONCLUSION

The kinetic heterogeneity of DCE-MRI can effectively distinguish between benign and malignant breast tumours and improve the specificity of BC diagnosis.

Evaluation of Breast Cancer Size Measurement by Computer-Aided Diagnosis (CAD) and a Radiologist on Breast MRI

Purpose: This study aimed to evaluate cancer size measurement by computer-aided diagnosis (CAD) and radiologist on breast magnetic resonance imaging (MRI) relative to histopathology and to determine clinicopathologic and MRI factors that may affect measurements. Methods: Preoperative MRI of 208 breast cancers taken between January 2017 and March 2021 were included. We evaluated correlation between CAD-generated size and pathologic size as well as that between radiologist-measured size and pathologic size. We classified size discrepancies into accurate and inaccurate groups. For both CAD and radiologist, clinicopathologic and imaging factors were compared between accurate and inaccurate groups. Results: The mean sizes as predicted by CAD, radiologist and pathology were 2.66 ± 1.68 cm, 2.54 ± 1.68 cm, and 2.30 ± 1.61 cm, with significant difference (p < 0.001). Correlation coefficients of cancer size measurement by radiologist and CAD in reference to pathology were 0.898 and 0.823. Radiologist’s measurement was more accurate than CAD, with statistical significance (p < 0.001). CAD-generated measurement was significantly more inaccurate for cancers of larger pathologic size (>2 cm), in the presence of an extensive intraductal component (EIC), with positive progesterone receptor (PR), and of non-mass enhancement (p = 0.045, 0.045, 0.03 and 0.002). Radiologist-measured size was significantly more inaccurate for cancers in presence of an in situ component, EIC, positive human epidermal growth factor receptor 2 (HER2), and non-mass enhancement (p = 0.017, 0.008, 0.003 and <0.001). Conclusion: Breast cancer size measurement showed a very strong correlation between CAD and pathology and radiologist and pathology. Radiologist-measured size was significantly more accurate than CAD size. Cancer size measurement by CAD and radiologist can both be inaccurate for cancers with EIC or non-mass enhancement.

Kinetic information from dynamic contrast-enhanced MRI enables prediction of residual cancer burden and prognosis in triple-negative breast cancer: a retrospective study

This study aimed to evaluate the predictions of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for prognosis of triple-negative breast cancer (TNBC), especially with residual disease (RD) after preoperative chemotherapy. This retrospective analysis included 74 TNBC patients who received preoperative chemotherapy. DCE-MRI findings from three timepoints were examined: at diagnosis (MRI_pre), at midpoint (MRI_mid) and after chemotherapy (MRI_post). These findings included cancer lesion size, washout index (WI) as a kinetic parameter using the difference in signal intensity between early and delayed phases, and time-signal intensity curve types. Distant disease-free survival was analysed using the log-rank test to compare RD group with and without a fast-washout curve. The diagnostic performance of DCE-MRI findings, including positive predictive value (PPV) for pathological responses, was also calculated. RD without fast washout curve was a significantly better prognostic factor, both at MRI_mid and MRI_post (hazard ratio = 0.092, 0.098, p < 0.05). PPV for pathological complete remission at MRI_mid was 76.7% by the cut-off point at negative WI value or lesion size = 0, and 66.7% at lesion size = 0. WI and curve types derived from DCE-MRI at the midpoint of preoperative chemotherapy can help not only assess tumour response but also predict prognosis.

Neoadjuvant Endocrine Therapy in Breast Cancer Management: State of the Art

Endocrine therapy is the mainstay of treatment in HR+/HER2- breast cancers, which represent about 70% of all breast cancers. Neoadjuvant therapy has been developed since the 1990s to address several issues, including breast-conserving surgery (BCS) and improvement of survival rates. For a long time, neoadjuvant endocrine therapy (NET) was confined to frail patients in order to improve surgery outcome. Since the 2000s, NET now plays a central role as a research tool for predictive endocrine sensitivity biomarkers and targeted therapies. One of the major issues in early HR+/HER2- breast cancer is to identify patients in whom chemotherapy can be safely withheld. In vivo assessment of response to NET might be the best treatment strategy to address this issue.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy

BACKGROUND

We aimed to investigate an automated semi-quantitative software as an imaging biomarker for the prediction of tissue response (TR) after completion of neoadjuvant chemotherapy (NAC).

METHODS

Breast magnetic resonance imaging (MRI) (1.5T, protocol according to international recommendations) of 67 patients with biopsy-proven invasive breast cancer were examined before and after NAC. After completion of NAC, histopathologic assessments of TR were classified according to the Chevallier grading system (CG1/4: full/non-responder; CG2/C3: partial responder). A commercially available fully automatic software (CADstream) extracted MRI parameters of tumor extension (tumor diameter/volume: TD/TV). Pre- versus post-NAC values were compared (ΔTV and ΔTD). Additionally, the software performed volumetric analyses of vascularization (VAV) after NAC. Accuracy of MRI parameters to predict TR were identified (cross-tabs, ROC, AUC, Kruskal-Wallis).

RESULTS

There were 37 (34.3%) CG1, 7 (6.5%) CG2, 53 (49.1%) CG3, and 11 (10.2%) CG4 lesions. The software reached area under the curve levels of 79.5% (CG1/complete response: ΔTD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: ΔTV).

CONCLUSION

Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.

Reproducibility of residual cancer burden for prognostic assessment of breast cancer after neoadjuvant chemotherapy

The residual cancer burden index was developed as a method to quantify residual disease ranging from pathological complete response to extensive residual disease. The aim of this study was to evaluate the inter-Pathologist reproducibility in the residual cancer burden index score and category, and in their long-term prognostic utility. Pathology slides and pathology reports of 100 cases from patients treated in a randomized neoadjuvant trial were reviewed independently by five pathologists. The size of tumor bed, average percent overall tumor cellularity, average percent of the in situ cancer within the tumor bed, size of largest axillary metastasis, and number of involved nodes were assessed separately by each pathologist and residual cancer burden categories were assigned to each case following calculation of the numerical residual cancer burden index score. Inter-Pathologist agreement in the assessment of the continuous residual cancer burden score and its components and agreement in the residual cancer burden category assignments were analyzed. The overall concordance correlation coefficient for the agreement in residual cancer burden score among pathologists was 0.931 (95% confidence interval (CI) 0.908-0.949). Overall accuracy of the residual cancer burden score determination was 0.989. The kappa coefficient for overall agreement in the residual cancer burden category assignments was 0.583 (95% CI 0.539-0.626). The metastatic component of the residual cancer burden index showed stronger concordance between pathologists (overall concordance correlation coefficient=0.980; 95% CI 0.954-0.992), than the primary component (overall concordance correlation coefficient=0.795; 95% CI 0.716-0.853). At a median follow-up of 12 years residual cancer burden determined by each of the pathologists had the same prognostic accuracy for distant recurrence-free and survival (overall concordance correlation coefficient=0.995; 95% CI 0.989-0.998). Residual cancer burden assessment is highly reproducible, with reproducible long-term prognostic significance.

Use of Standardized Uptake Value Ratios Decreases Interreader Variability of [18F] Florbetapir PET Brain Scan Interpretation

BACKGROUND AND PURPOSE

Fluorine-18 florbetapir is a recently developed β-amyloid plaque positron-emission tomography imaging agent with high sensitivity, specificity, and accuracy in the detection of moderate-to-frequent cerebral cortical β-amyloid plaque. However, the FDA has expressed concerns about the consistency of interpretation of [(18)F] florbetapir PET brain scans. We hypothesized that incorporating automated cerebral-to-whole-cerebellar standardized uptake value ratios into [(18)F] florbetapir PET brain scan interpretation would reduce this interreader variability.

MATERIALS AND METHODS

This randomized, blinded-reader study used previously acquired [(18)F] florbetapir scans from 30 anonymized patients who were enrolled in the Alzheimer's Disease Neuroimaging Initiative 2. In 4 separate, blinded-reading sessions, 5 readers classified 30 cases as positive or negative for significant β-amyloid deposition either qualitatively alone or qualitatively with additional adjunct software that determined standardized uptake value ratios. A κ coefficient was used to calculate interreader agreement with and without the use of standardized uptake value ratios.

RESULTS

There was complete interreader agreement on 20/30 cases of [(18)F] florbetapir PET brain scans by using qualitative interpretation and on 27/30 scans interpreted with the adjunct use of standardized uptake value ratios. The κ coefficient for the studies read with standardized uptake value ratios (0.92) was significantly higher compared with the qualitatively read studies (0.69, P = .006).

CONCLUSIONS

Use of standardized uptake value ratios improves interreader agreement in the interpretation of [(18)F] florbetapir images.

Response to neoadjuvant treatment of invasive ductal breast carcinomas including outcome evaluation: MRI analysis by an automatic CAD system in comparison to visual evaluation

BACKGROUND

The aim of this study was to evaluate imaging-based response to standardized neoadjuvant chemotherapy (NACT) regimen by dynamic contrast-enhanced magnetic resonance mammography (DCE-MRM), whereas MR images were analyzed by an automatic computer-assisted diagnosis (CAD) system in comparison to visual evaluation. MRI findings were correlated with histopathologic response to NACT and also with the occurrence of metastases in a follow-up analysis.

PATIENTS AND METHODS

Fifty-four patients with invasive ductal breast carcinomas received two identical MRI examinations (before and after NACT; 1.5T, contrast medium gadoteric acid). Pre-therapeutic images were compared with post-therapeutic examinations by CAD and two blinded human observers, considering morphologic and dynamic MRI parameters as well as tumor size measurements. Imaging-assessed response to NACT was compared with histopathologically verified response. All clinical, histopathologic, and DCE-MRM parameters were correlated with the occurrence of distant metastases.

RESULTS

Initial and post-initial dynamic parameters significantly changed between pre- and post-therapeutic DCE-MRM. Visually evaluated DCE-MRM revealed sensitivity of 85.7%, specificity of 91.7%, and diagnostic accuracy of 87.0% in evaluating the response to NACT compared to histopathology. CAD analysis led to more false-negative findings (37.0%) compared to visual evaluation (11.1%), resulting in sensitivity of 52.4%, specificity of 100.0%, and diagnostic accuracy of 63.0%. The following dynamic MRI parameters showed significant associations to occurring metastases: Post-initial curve type before NACT (entire lesions, calculated by CAD) and post-initial curve type of the most enhancing tumor parts after NACT (calculated by CAD and manually).

CONCLUSIONS

In the accurate evaluation of response to neoadjuvant treatment, CAD systems can provide useful additional information due to the high specificity; however, they cannot replace visual imaging evaluation. Besides traditional prognostic factors, contrast medium-induced dynamic MRI parameters reveal significant associations to patient outcome, i.e. occurrence of distant metastases.

The role of magnetic resonance imaging in assessing residual disease and pathologic complete response in breast cancer patients receiving neoadjuvant chemotherapy: a systematic review

OBJECTIVES

This systematic review aimed to assess the role of magnetic resonance imaging (MRI) in evaluating residual disease extent and the ability to detect pathologic complete response (pCR) after neoadjuvant chemotherapy for invasive breast cancer.

METHODS

PubMed, the Cochrane Library, MEDLINE, and Embase databases were searched for relevant studies published until 1 July 2012. After primary selection, two reviewers independently assessed the content of each eligible study using a standardised extraction form and pre-defined inclusion and exclusion criteria.

RESULTS

A total of 35 eligible studies were selected. Correlation coefficients of residual tumour size assessed by MRI and pathology were good, with a median value of 0.698. Reported sensitivity, specificity, positive predictive value and negative predictive value for predicting pCR with MRI ranged from 25 to 100 %, 50-97 %, 47-73 % and 71-100 %, respectively. Both overestimation and underestimation were observed. MRI proved more accurate in determining residual disease than physical examination, mammography and ultrasound. Diagnostic accuracy of MRI after neoadjuvant chemotherapy could be influenced by treatment regimen and breast cancer subtype.

CONCLUSIONS

Breast MRI accuracy for assessing residual disease after neoadjuvant chemotherapy is good and surpasses other diagnostic means. However, both overestimation and underestimation of residual disease extent could be observed.

MAIN MESSAGES

• Breast MRI accuracy for assessing residual disease is good and surpasses other diagnostic means. • Correlation coefficients of residual tumour size assessed by MRI and pathology were considered good. • However, both overestimation and underestimation of residual disease were observed. • Diagnostic accuracy of MRI seems to be affected by treatment regimen and breast cancer subtype.