Factors associated with MRI detection of occult lesions in newly diagnosed breast cancers

Ultrafast sequence-based prediction model and nomogram to differentiate additional suspicious lesions on preoperative breast MRI

OBJECTIVES

To investigate whether ultrafast sequence improves the diagnostic performance of conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating additional suspicious lesions (ASLs) on preoperative breast MRI.

MATERIALS AND METHODS

A retrospective database search identified 668 consecutive patients who underwent preoperative breast DCE-MRI with ultrafast sequence between June 2020 and July 2021. Among these, 107 ASLs from 98 patients with breast cancer (36 multifocal, 42 multicentric, and 29 contralateral) were identified. Clinical, pathological, conventional MRI findings, and ultrafast sequence-derived parameters were collected. A prediction model that adds ultrafast sequence-derived parameters to clinical, pathological, and conventional MRI findings was developed and validated internally. Decision curve analysis and net reclassification index statistics were performed. A nomogram was constructed.

RESULTS

The ultrafast model adding time to peak enhancement, time to enhancement, and maximum slope showed a significantly increased area under the receiver operating characteristic curve compared with the conventional model which includes age, human epidermal growth factor receptor 2 expression of index cancer, size of index cancer, lesion type of index cancer, location of ASL, and size of ASL (0.92 vs. 0.82; p = 0.002). The decision curve analysis showed that the ultrafast model had a higher overall net benefit than the conventional model. The net reclassification index of ultrafast model was 23.3% (p = 0.001).

CONCLUSION

A combination of ultrafast sequence-derived parameters with clinical, pathological, and conventional MRI findings can aid in the differentiation of ASL on preoperative breast MRI.

CLINICAL RELEVANCE STATEMENT

Our prediction model and nomogram that was based on ultrafast sequence-derived parameters could help radiologists differentiate ASLs on preoperative breast MRI.

KEY POINTS

Ultrafast MRI can diminish background parenchymal enhancement and possibly improve diagnostic accuracy for additional suspicious lesions (ASLs). Location of ASL, larger size of ASL, and higher maximum slope were associated with malignant ASL. The ultrafast model and nomogram can help preoperatively differentiate additional malignancies.

A Multi-institutional Analysis of Factors Influencing the Rate of Positive MRI Biopsy Among Women with Early-Stage Breast Cancer

BACKGROUND

The use of preoperative magnetic resonance imaging (MRI) for early-stage breast cancer (ESBC) is increasing, but its utility in detecting additional malignancy is unclear and delays surgical management (Jatoi and Benson in Future Oncol 9:347-353, 2013. https://doi.org/10.2217/fon.12.186 , Bleicher et al. J Am Coll Surg 209:180-187, 2009. https://doi.org/10.1016/j.jamcollsurg.2009.04.010 , Borowsky et al. J Surg Res 280:114-122, 2022. https://doi.org/10.1016/j.jss.2022.06.066 ). The present study sought to identify ESBC patients most likely to benefit from preoperative MRI by assessing the positive predictive values (PPVs) of ipsilateral and contralateral biopsies.

METHODS

A retrospective cohort study included patients with cTis-T2N0-N1 breast cancer from two institutions during 2016-2021. A "positive" biopsy result was defined as additional cancer (PositiveCancer) or cancer with histology often excised (PositiveSurg). The PPV of MRI biopsies was calculated with respect to age, family history, breast density, and histology. Uni- and multivariate logistic regression determined whether combinations of age younger than 50 years, dense breasts, family history, and pure ductal carcinoma in situ (DCIS) histology led to higher biopsy yield.

RESULTS

Of the included patients, 447 received preoperative MRI and 131 underwent 149 MRI-guided biopsies (96 ipsilateral, 53 contralateral [18 bilateral]). PositiveCancer for ipsilateral biopsy was 54.2%, and PositiveCancer for contralateral biopsy was 17.0%. PositiveSurg for ipsilateral biopsy was 62.5%, and PositiveSurg for contralateral biopsy was 24.5%. Among the contralateral MRI biopsies, patients younger than 50 years were less likely to have PositiveSurg (odds ratio, 0.02; 95% confidence interval, 0.00-0.84; p = 0.041). The combinations of age, density, family history, and histology did not lead to a higher biopsy yield.

CONCLUSION

Historically accepted factors for recommending preoperative MRI did not appear to confer a higher MRI biopsy yield. To prevent delays to surgical management, MRI should be carefully selected for individual patients most likely to benefit from additional imaging.

The features associated with mammography-occult MRI-detected newly diagnosed breast cancer analysed by comparing machine learning models with a logistic regression model

PURPOSE

To compare machine learning (ML) models with logistic regression model in order to identify the optimal factors associated with mammography-occult (i.e. false-negative mammographic findings) magnetic resonance imaging (MRI)-detected newly diagnosed breast cancer (BC).

MATERIAL AND METHODS

The present single-centre retrospective study included consecutive women with BC who underwent mammography and MRI (no more than 45 days apart) for breast cancer between January 2018 and May 2023. Various ML algorithms and binary logistic regression analysis were utilized to extract features linked to mammography-occult BC. These features were subsequently employed to create different models. The predictive value of these models was assessed using receiver operating characteristic curve analysis.

RESULTS

This study included 1957 malignant lesions from 1914 patients, with an average age of 51.64 ± 9.92 years and a range of 20-86 years. Among these lesions, there were 485 mammography-occult BCs. The optimal features of mammography-occult BC included calcification status, tumour size, mammographic density, age, lesion enhancement type on MRI, and histological type. Among the different ML models (ANN, L1-LR, RF, and SVM) and the LR-based combined model, the ANN model with RF features was found to be the optimal model. It demonstrated the best discriminative performance in predicting mammography false- negative findings, with an AUC of 0.912, an accuracy of 86.90%, a sensitivity of 85.85%, and a specificity of 84.18%.

CONCLUSION

Mammography-occult MRI-detected breast cancers have features that should be considered when performing breast MRI to improve the detection rate for breast cancer and aid in clinician management.

Which Patients With Newly Diagnosed Breast Cancer Benefit From Preoperative Magnetic Resonance Imaging?

Objective

The aim of this study was to identify the effectiveness and selective applications of preoperative magnetic resonance imaging (MRI) by investigating clinicopathologic factors of the index tumor with or without false lesions on MRI.

Summary of background data

Preoperative MRI is commonly performed in patients with newly diagnosed breast cancer, but its clinical significance is unclear.

Methods

A total of 103 breast cancer patients who had undergone MRI or ultrasound followed by mastectomy were included in this retrospective investigation of pathologic, clinical, and imaging findings.

Results

MRI showed 29 false-positive lesions in 57 patients, 5 false-negative lesions in 5 patients, and 69 true-positive lesions in 103 patients. More false lesions on MRI were found in patients with more lesions on ultrasound, small-sized index tumors on ultrasound, or early-stage cancer. The sensitivity of MRI and ultrasound were 96.5% and 92.3% (P = 0.119), respectively, and the positive predictive value of them were 71.5% and 72.5% (P = 0.828), respectively.

Conclusions

Preoperative MRI is more useful in patients with newly diagnosed breast cancer who have large-sized or more advanced cancers or fewer lesions on ultrasound.

Does the Addition of Breast MRI Add Value to the Diagnostic Workup of Invasive Lobular Carcinoma?

BACKGROUND

Invasive lobular carcinoma (ILC) has unique histologic growth pattern. Few studies have focused on the value of breast magnetic resonance imaging (MRI) specifically for ILC. We hypothesized that MRI adds value to the diagnostic workup in ILC by better defining the extent of disease and identifying additional foci of malignancy, which can change the surgical plan.

MATERIALS AND METHODS

This was a single-institution retrospective review of women diagnosed with ILC from 1/2012 to 7/2019 who underwent preoperative MRI. Patient, tumor characteristics, and initial surgical plan were reviewed. MRI had added value if ILC size correlated best to final pathologic size or if additional malignancy was identified. MRI was considered harmful if additional biopsies were benign or if the size was overestimated.

RESULTS

ILC was identified in 166 breasts in 165 women. Original surgical plan was for lumpectomy in 86 (52%), mastectomy in 49 (30%), and undecided in 31 (18%). MRI changed the plan in 25 (19%) with 24 (96%) changing from lumpectomy to mastectomy. Additional biopsy was performed in 28% after MRI, the majority (n = 41, 72%) were benign or high risk and 16 (28%) identified additional malignancy. MRI was not a better size estimate than mammogram/ultrasound. Re-excision rate after lumpectomy was 6.8% (5/73). MRI added value in 48 (28.9%) and was harmful in 48 (28.9%).

CONCLUSIONS

Using breast MRI in the diagnostic workup of ILC has both positive and negative implications on surgical treatment planning. A shared decision-making conversation is warranted before proceeding with MRI to maximize value and minimize harms associated with this diagnostic tool.