The Value of Patient and Tumor Factors in Predicting Preoperative Breast MRI Outcomes

Discriminative Factors of Malignancy of Ipsilateral Nonmass Enhancement in Women With Newly Diagnosed Breast Cancer on Initial Staging Breast MRI

BACKGROUND

Nonmass enhancement (NME) on breast MRI impacts surgical planning.

PURPOSE

To evaluate positive predictive values (PPVs) and identify malignancy discriminators of NME ipsilateral to breast cancer on initial staging MRI.

STUDY TYPE

Retrospective.

SUBJECTS

Eighty-six women (median age, 48 years; range, 26-75 years) with 101 NME lesions (BI-RADS 4 and 5) ipsilateral to known cancers and confirmed histopathology.

FIELD STRENGTH/SEQUENCE

1.5 T and 3.0 T dynamic contrast-enhanced fat-suppressed T1-weighted fast spoiled gradient-echo.

ASSESSMENT

Three radiologists blinded to pathology independently reviewed MRI features (distribution, internal enhancement pattern, and enhancement kinetics) of NME, locations relative to index cancers (contiguous, non-contiguous, and different quadrants), associated mammographic calcifications, lymphovascular invasion (LVI), axillary node metastasis, and radiology-pathology correlations. Clinical factors, NME features, and cancer characteristics were analyzed for associations with NME malignancy.

STATISTICAL TESTS

Fisher's exact, Chi-square, Wilcoxon rank sum tests, and mixed-effect multivariable logistic regression were used. Significance threshold was set at P < 0.05.

RESULTS

Overall NME malignancy rate was 48.5% (49/101). Contiguous NME had a significantly higher malignancy rate (86.7%) than non-contiguous NME (25.0%) and NME in different quadrants (10.7%), but no significant difference was observed by distance from cancer for non-contiguous NME, P = 0.68. All calcified NME lesions contiguous to the calcified index cancer were malignant. NME was significantly more likely malignant when index cancers were masses compared to NME (52.9% vs. 21.4%), had mammographic calcifications (63.2% vs. 39.7%), LVI (81.8% vs. 44.4%), and axillary node metastasis (70.8% vs. 41.6%). NME features with highest PPVs were segmental distribution (85.7%), clumped enhancement (66.7%), and nonpersistent kinetics (77.1%). On multivariable analysis, contiguous NME, segmental distribution, and nonpersistent kinetics were associated with malignancy.

DATA CONCLUSION

Malignancy discriminators of ipsilateral NME on staging MRI included contiguous location to index cancers, segmental distribution, and nonpersistent kinetics.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Machine learning and new insights for breast cancer diagnosis

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning

Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present an open-source machine learning framework, AutoPrognosis 2.0, to facilitate the development of diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. We have implemented our risk score as a web-based decision support tool, which can be publicly accessed by patients and clinicians. By open-sourcing our framework as a tool for the community, we aim to provide clinicians and other medical practitioners with an accessible resource to develop new risk scores, personalized diagnostics, and prognostics using machine learning techniques. Software: https://github.com/vanderschaarlab/AutoPrognosis.

Preoperative Breast MRI: Current Evidence and Patient Selection

Breast MRI is the most sensitive imaging modality for the assessment of newly diagnosed breast cancer extent and can detect additional mammographically and clinically occult breast cancers in the ipsilateral and contralateral breasts. Nonetheless, appropriate use of breast MRI in the setting of newly diagnosed breast cancer remains debated. Though highly sensitive, MRI is less specific and may result in false positives and overestimation of disease when MRI findings are not biopsied prior to surgical excision. Furthermore, improved anatomic depiction of breast cancer on MRI has not consistently translated to improved clinical outcomes, such as lower rates of re-excision or breast cancer recurrence, though there is a paucity of well-designed studies examining these issues. In addition, current treatment paradigms have been developed in the absence of this more accurate depiction of disease span, which likely has limited the value of MRI. These issues have led to inconsistent and variable utilization of preoperative MRI across practice settings and providers. In this review, we discuss the history of breast MRI and its current use and recommendations with a focus on the preoperative setting. We review the evidence surrounding the use of preoperative MRI in the evaluation of breast malignancies and discuss the data on breast MRI in the setting of specific patient factors often used to determine breast MRI eligibility, such as age, index tumor phenotype, and breast density. Finally, we review the impact of breast MRI on surgical outcomes (re-excision and mastectomy rates) and long-term breast recurrence and survival outcomes.

Preoperative MRI in breast cancer: effect of breast density on biopsy rate and yield

PURPOSE

Preoperative breast MRI is used to evaluate for additional cancer and extent of disease for newly diagnosed breast cancer, yet benefits and harms of preoperative MRI are not well-documented. We examined whether preoperative MRI yields additional biopsy and cancer detection by extent of breast density.

METHODS

We followed women in the Breast Cancer Surveillance Consortium with an incident breast cancer diagnosed from 2005 to 2017. We quantified breast biopsies and cancers detected within 6 months of diagnosis by preoperative breast MRI receipt, overall and by breast density, accounting for MRI selection bias using inverse probability weighted logistic regression.

RESULTS

Among 19,324 women with newly diagnosed breast cancer, 28% had preoperative MRI, 11% additional biopsy, and 5% additional cancer detected. Four times as many women with preoperative MRI underwent additional biopsy compared to women without MRI (22.6% v. 5.1%). Additional biopsy rates with preoperative MRI increased with increasing breast density (27.4% for extremely dense compared to 16.2% for almost entirely fatty breasts). Rates of additional cancer detection were almost four times higher for women with v. without MRI (9.9% v. 2.6%). Conditional on additional biopsy, age-adjusted rates of additional cancer detection were lowest among women with extremely dense breasts, regardless of imaging modality (with MRI: 35.0%; 95% CI 27.0-43.0%; without MRI: 45.1%; 95% CI 32.6-57.5%).

CONCLUSION

For women with dense breasts, preoperative MRI was associated with much higher biopsy rates, without concomitant higher cancer detection. Preoperative MRI may be considered for some women, but selecting women based on breast density is not supported by evidence.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT02980848; registered 2017.

Accuracy of Preoperative Breast MRI Versus Conventional Imaging in Measuring Pathologic Extent of Invasive Lobular Carcinoma

OBJECTIVE

To determine whether invasive lobular carcinoma (ILC) extent is more accurately depicted with preoperative MRI (pMRI) than conventional imaging (mammography and/or ultrasound).

METHODS

After IRB approval, we retrospectively identified women with pMRIs (February 2005 to January 2014) to evaluate pure ILC excluding those with ipsilateral pMRI BI-RADS 4 or 5 findings or who had neoadjuvant chemotherapy. Agreement between imaging and pathology sizes was summarized using Bland-Altman plots, absolute and percent differences, and the intraclass correlation coefficient (ICC). Rates of underestimation and overestimation were evaluated and their associations with clinical features were explored.

RESULTS

Among the 56 women included, pMRI demonstrated better agreement with pathology than conventional imaging by mean absolute difference (1.6 mm versus -7.8 mm, P < 0.001), percent difference (10.3% versus -16.4%, P < 0.001), and ICC (0.88 versus 0.61, P = 0.019). Conventional imaging more frequently underestimated ILC span than pMRI using a 5 mm difference threshold (24/56 (43%) versus 10/56 (18%), P < 0.001), a 25% threshold (19/53 (36%) versus 10/53 (19%), P = 0.035), and T category change (17/56 (30%) versus 7/56 (13%), P = 0.006). Imaging-pathology size concordance was greater for MRI-described solitary masses than other lesions for both MRI and conventional imaging (P < 0.05). Variability of conventional imaging was lower for patients ≥ the median age of 62 years than for younger patients (SD: 12 mm versus 22 mm, P = 0.012).

CONCLUSION

MRI depicts pure ILC more accurately than conventional imaging and may have particular value for younger women.