Pathologic High-risk Lesions, Diagnosis and Management

Assessing the Correlation between Allostatic Load and False-Positive Image-Guided Breast Biopsies

Background: Allostatic load (AL) is the accumulation of physiological dysregulation attributed to repeated activation of the stress response over a lifetime. We assessed the utility of AL as a prognostic measure for high-risk benign breast biopsy pathology results. Method: Eligible patients were women 18 years or older, with a false-positive outpatient breast biopsy between January and December 2022 at a tertiary academic health center. AL was calculated using 12 variables representing four physiological systems: cardiovascular (pulse rate, systolic and diastolic blood pressures, total cholesterol, high-density lipoprotein, and low-density lipoprotein); metabolic (body mass index, albumin, and hemoglobin A1C); renal (creatinine and estimated glomerular filtration rate); and immune (white blood cell count). Multivariable logistic regression was used to assess the association between AL before biopsy and breast biopsy outcomes controlling for patients' sociodemographics. Results: In total, 170 women were included (mean age, 54.1 ± 12.9 years): 89.4% had benign and 10.6% had high-risk pathologies (radial scar/complex sclerosing lesion, atypical ductal or lobular hyperplasia, flat epithelial atypia, intraductal papilloma, or lobular carcinoma in-situ). A total of 56.5% were White, 24.7% Asian, and 17.1% other races. A total of 32.5% identified as Hispanic. The mean breast cancer risk score using the Tyrer-Cuzick model was 11.9 ± 7.0. In multivariable analysis, with every one unit increase in AL, the probability of high-risk pathology increased by 37% (odds ratio, 1.37; 95% confidence interval, 1.03, 1.81; p = 0.03). No significant association was seen between high-risk pathology and age, ethnicity, breast cancer risk, or area deprivation index. Conclusion: Our findings support that increased AL, a biological marker of stress, is associated with high-risk pathology among patients with false-positive breast biopsy results.

High-Risk Lesion Management

High-risk lesions or lesions of uncertain malignant potential are frequent findings on image-guided needle biopsy of the breast and comprise a number of distinct entities. These lesions are known for having risk of underlying malignancy and are usually associated with an increased lifetime risk for breast cancer. Surgical excision was traditionally recommended for all high-risk lesions but recent studies have demonstrated that vacuum-assisted excision or surveillance may be adequate for some lesions. While management of high-risk lesion varies among institutions, this chapter describes the management recommendations based on recent literature of the most frequent types of lesions.

Atypical Ductal Hyperplasia after Vacuum-Assisted Breast Biopsy: Can We Reduce the Upgrade to Breast Cancer to an Acceptable Rate?

(1) Background: to evaluate which factors can reduce the upgrade rate of atypical ductal hyperplasia (ADH) to in situ or invasive carcinoma in patients who underwent vacuum-assisted breast biopsy (VABB) and subsequent surgical excision. (2) Methods: 2955 VABBs were reviewed; 141 patients with a diagnosis of ADH were selected for subsequent surgical excision. The association between patients’ characteristics and the upgrade rate to breast cancer was evaluated in both univariate and multivariate analyses. (3) Results: the upgrade rates to ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) were, respectively, 29.1% and 7.8%. The pooled upgrade rate to DCIS or IC was statistically lower at univariate analysis, considering the following parameters: complete removal of the lesion (p-value < 0.001); BIRADS ≤ 4a (p-value < 0.001); size of the lesion ≤15 mm (p-value: 0.002); age of the patients <50 years (p-value: 0.035). (4) Conclusions: the overall upgrade rate of ADH to DCIS or IC is high and, as already known, surgery should be recommended. However, ADH cases should always be discussed in multidisciplinary meetings: some parameters appear to be related to a lower upgrade rate. Patients presenting these parameters could be strictly followed up to avoid overtreatment.

Molecular fingerprint of precancerous lesions in breast atypical hyperplasia

OBJECTIVE

To identify atypical hyperplasia (AH) of the breast by shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), and to explore the molecular fingerprinting characteristics of breast AH.

METHODS

Breast hyperplasia was studied in 11 hospitals across China from January 2015 to December 2016. All patients completed questionnaires on women's health. The differences between patients with and without breast AH were compared. AH breast lesions were detected by Raman spectroscopy followed by the SHINERS technique.

RESULTS

There were no significant differences in clinical features and risk-related factors between patients with breast AH (n = 37) and the control group (n = 2576). Fifteen cases of breast AH lesions were detected by Raman spectroscopy. The main different Raman peaks in patients with AH appeared at 880, 1001, 1086, 1156, 1260, and 1610 cm^-1, attributed to the different vibrational modes of nucleic acids, β-carotene, and proteins. Shell-isolated nanoparticles had different enhancement effects on the nucleic acid, protein, and lipid components in AH.

CONCLUSION

Raman spectroscopy can detect characteristic molecular changes in breast AH lesions, and may thus be useful for the non-invasive early diagnosis and for investigating the mechanism of tumorigenesis in patients with breast AH.

Performance of a clinical and imaging-based multivariate model as decision support tool to help save unnecessary surgeries for high-risk breast lesions

PURPOSE

To investigate the performance of an imaging and biopsy parameters-based multivariate model in decreasing unnecessary surgeries for high-risk breast lesions.

METHODS

In an IRB-approved study, we retrospectively reviewed all high-risk lesions (HRL) identified at imaging-guided biopsy in our institution between July 1, 2014-July 1, 2017. Lesions were categorized high-risk-I (HR-I = atypical ductal hyperplasia, atypical lobular hyperplasia, lobular carcinoma in situ and atypical papillary lesion) and II (HR-II = Flat epithelial atypia, radial scar, benign papilloma). Patient risk factors, lesion features, detection and biopsy modality, excision and cancer upgrade rates were collected. Reference standard for upgrade was either excision or at least 2-year imaging follow-up. Multiple logistic regression analysis was performed to develop a multivariate model using HRL type, lesion and biopsy needle size for surgical cancer upgrade with performance assessed using ROC analysis.

RESULTS

Of 699 HRL in 652 patients, 525(75%) had reference standard available, and 48/525(9.1%) showed cancer at surgical excision. Excision (84.5% vs 51.1%) and upgrade (17.6%vs1.8%) rates were higher in HR-I compared to HR-II (p < 0.01). In HR-I, small needle size < 12G vs ≥ 12G [32.1% vs 13.2%, p < 0.01] and less cores [< 6 vs ≥ 6, 28.6%vs13.7%, p = 0.01] were significantly associated with higher cancer upgrades. Our multivariate model had an AUC = 0.87, saving 28.1% of benign surgeries with 100% sensitivity, based on HRL subtype, lesion size(mm, continuous), needle size (< 12G vs ≥ 12G) and biopsy modality (US vs MRI vs stereotactic) CONCLUSION: Our multivariate model using lesion size, needle size and patient age had a high diagnostic performance in decreasing unnecessary surgeries and shows promise as a decision support tool.

Explainable AI (xAI) for Anatomic Pathology

Pathologists are adopting whole slide images (WSIs) for diagnosis, thanks to recent FDA approval of WSI systems as class II medical devices. In response to new market forces and recent technology advances outside of pathology, a new field of computational pathology has emerged that applies artificial intelligence (AI) and machine learning algorithms to WSIs. Computational pathology has great potential for augmenting pathologists' accuracy and efficiency, but there are important concerns regarding trust of AI due to the opaque, black-box nature of most AI algorithms. In addition, there is a lack of consensus on how pathologists should incorporate computational pathology systems into their workflow. To address these concerns, building computational pathology systems with explainable AI (xAI) mechanisms is a powerful and transparent alternative to black-box AI models. xAI can reveal underlying causes for its decisions; this is intended to promote safety and reliability of AI for critical tasks such as pathology diagnosis. This article outlines xAI enabled applications in anatomic pathology workflow that improves efficiency and accuracy of the practice. In addition, we describe HistoMapr-Breast, an initial xAI enabled software application for breast core biopsies. HistoMapr-Breast automatically previews breast core WSIs and recognizes the regions of interest to rapidly present the key diagnostic areas in an interactive and explainable manner. We anticipate xAI will ultimately serve pathologists as an interactive computational guide for computer-assisted primary diagnosis.