Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA),  L. E. Elshof et al., Eur J Cancer, 51, 1497-510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. OBJECTIVE: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. METHODS: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. RESULTS: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. CONCLUSION: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.

Contrast-enhanced ultrasound to predict malignant upgrading of atypical ductal hyperplasia.

BACKGROUND: A malignancy might be found at surgery in cases of atypical ductal hyperplasia (ADH) diagnosed via US-guided core needle biopsy (CNB). The objective of this study was to investigate the diagnostic performance of contrast-enhanced ultrasound (CEUS) in predicting ADH diagnosed by US-guided CNB that was upgraded to malignancy after surgery. METHODS: In this retrospective study, 110 CNB-diagnosed ADH lesions in 109 consecutive women who underwent US, CEUS, and surgery between June 2018 and June 2023 were included. CEUS was incorporated into US BI-RADS and yielded a CEUS-adjusted BI-RADS. The diagnostic performance of US BI-RADS and CEUS-adjusted BI-RADS for ADH were analyzed and compared. RESULTS: The mean age of the 109 women was 49.7 years ± 11.6 (SD). The upgrade rate of ADH at CNB was 48.2% (53 of 110). The sensitivity, specificity, positive predictive value, and negative predictive value of CEUS for identification of malignant upgrading were 96.2%, 66.7%,72.9%, and 95.0%, respectively, based on BI-RADS category 4B threshold. The two false-negative cases were low-grade ductal carcinoma in situ. Compared with the US, CEUS-adjusted BI-RADS had better specificity for lesions smaller than 2 cm (76.7% vs. 96.7%, P = 0.031). After CEUS, 16 (10 malignant and 6 nonmalignant) of the 45 original US BI-RADS category 4A lesions were up-classified to BI-RADS 4B, and 3 (1 malignant and 2 nonmalignant) of the 41 original US BI-RADS category 4B lesions were down-classified to BI-RADS 4A. CONCLUSIONS: CEUS is helpful in predicting malignant upgrading of ADH, especially for lesions smaller than 2 cm and those classified as BI-RADS 4A and 4B on ultrasound.

Sonography-based multimodal information platform for identifying the surgical pathology of ductal carcinoma in situ.

BACKGROUND: The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making. PURPOSE: To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS. MATERIALS AND METHODS: Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC). RESULTS: In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net. CONCLUSION: By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.

Clinical and Imaging Features of MRI Screen-Detected Breast Cancer.

BACKGROUND: Supplemental screening with breast MRI is recommended annually for patients who have greater than 20% lifetime risk for breast cancer. While there is robust data regarding features of mammographic screen-detected breast cancers, there is limited data regarding MRI-screen-detected cancers. PATIENTS AND METHODS: Screening breast MRIs performed between August 1, 2016 and July 30, 2022 identified 50 screen-detected breast cancers in 47 patients. Clinical and imaging features of all eligible cancers were recorded. RESULTS: During the study period, 50 MRI-screen detected cancers were identified in 47 patients. The majority of MRI-screen detected cancers (32/50, 64%) were invasive. Pathology revealed ductal carcinoma in situ (DCIS) in 36% (18/50), invasive ductal carcinoma (IDC) in 52% (26/50), invasive lobular carcinoma in 10% (5/50), and angiosarcoma in 2% (1/50). The majority of patients (43/47, 91%) were stage 0 or 1 at diagnosis and there were no breast cancer-related deaths during the follow-up periods. Cancers presented as masses in 50% (25/50), nonmass enhancement in 48% (25/50), and a focus in 2% (1/50). DCIS was more likely to present as nonmass enhancement (94.4%, 17/18), whereas invasive cancers were more likely to present as masses (75%, 24/32) (P < .001). All cancers that were stage 2 at diagnosis were detected either on a baseline exam or more than 4 years since the prior MRI exam. CONCLUSION: MRI screen-detected breast cancers were most often invasive cancers. Cancers detected by MRI screening had an excellent prognosis in our study population. Invasive cancers most commonly presented as a mass.

Digital breast tomosynthesis versus X-ray of the breast specimen for intraoperative margin assessment: A randomized trial.

BACKGROUND: Involved resection margins after breast conserving surgery (BCS) often require a re-operation with increased patient anxiety and risk of impaired cosmesis. We investigated the number of re-operations due to involved resection margins after BCS comparing digital breast tomosynthesis(DBT) with X-ray for intraoperative margin evaluation. Furthermore, we assessed the diagnostic accuracy of these methods to predict histopathological margin status. Finally, we evaluated risk factors for re-operation. METHODS: In this randomized, non-blinded study, 250 invasive breast cancer patients were randomized (1:1), whereof 241 were analyzed intraoperatively with either DBT (intervention, n = 119) or X-ray (standard, n = 122). Pearson's chi-squared test, Fisher's exact test, t-test, logistic and ordinal regression analysis was used as appropriate. RESULTS: No difference was found in the number of re-operations between the DBT and X-ray group (16.8 % vs 19.7 %, p = 0.57), or in diagnostic accuracy to predict histopathological margin status (77.5 %, CI: 68.6-84.9 %) and (67.3 %, CI: 57.7-75.9 %), respectively. We evaluated 5 potential risk factors for re-operation: Ductal carcinoma in situ (DCIS) outside tumor, OR = 9.4 (CI: 4.3-20.6, p < 0.001); high mammographic breast density, OR = 6.1 (CI: 1.0-38.1, p = 0.047); non-evaluable margins on imaging, OR = 3.8 (CI: 1.3-10.8, p = 0.016); neoadjuvant chemotherapy, OR = 3.0 (CI: 1.0-8.8, p = 0.048); and T2 tumor-size, OR = 2.6 (CI: 1.0-6.4, p = 0.045). CONCLUSIONS: No difference was found in the number of re-operations or in diagnostic accuracy to predict histopathological margin status between DBT and X-ray groups. DCIS outside the tumor showed the highest risk of re-operation. Intraoperative methods with improved visualization of DCIS are needed to obtain tumor free margins in BCS.

Lobular Neoplasia Diagnosed by MRI-Guided Breast Biopsy: Identifying Upgrade Rate to Malignancy and Outcomes of Clinical and Surgical Management.

OBJECTIVE: The aim of this study was to determine surgical and clinical outcomes of lobular neoplasia (LN) diagnosed by magnetic resonance imaging (MRI) biopsy, including upgrade to malignancy, and to assess for characteristics associated with upgrade. METHOD: A single-institution retrospective study, between 2013 and 2022, of patients with histopathological findings of LN via MRI-guided biopsy was performed using an institutional database and review of the electronic medical records. Decision for excision or surveillance was made by a multidisciplinary team per institutional practice. Patient demographics and imaging characteristics were summarized using descriptive analyses. Upgrade was defined as upgrade to cancer on surgical pathology for patients treated with excision or the development of cancer at the biopsy site during surveillance. The Wilcoxon rank-sum test and Fisher's exact test were used to compare features of the upgraded cohort with the remainder of the group. RESULTS: Ninety-four MRI biopsies diagnosing LN were included. Median age was 57 years (range 37-78 years). Forty-six lesions underwent excision while 48 lesions were surveilled. The upgrade rate was 7.4% (7/94). Upgrades in the excised cohort consisted of pleomorphic lobular carcinoma in situ (LCIS; n = 1), ductal carcinoma in situ (DCIS; n = 3) and invasive lobular carcinoma (ILC; n = 2), while one interval development of DCIS was observed at the site of biopsy in the surveillance cohort. No MRI or patient variables were associated with upgrade. CONCLUSIONS: In this contemporary cohort of MRI-detected LNs, the upgrade rate was low. Omission of surgery for MRI-detected LNs in carefully selected patients may be considered in a shared decision-making capacity between the patient and the treatment team. Larger cohorts are needed to determine factors predictive of upgrade risk.

Perioperative care of nipple-areola complex-sparing mastectomy and one-stage breast reconstruction via endoscopic axillary approach for ductal carcinoma in situ: A case report.

RATIONALE: Breast cancer represents a prevalent malignancy that primarily impacts women, with pronounced consequences on their overarching health. The major therapeutic approach, encompassing surgical procedures, can often culminate in mastectomy, potentially inciting psychological turmoil and disorders. PATIENT CONCERNS: A patient was admitted to our facility on May 5, 2023, precipitated by the discovery of bilateral breast masses during a routine physical examination conducted 3 days before admission. DIAGNOSIS: The breasts were symmetric, with the right nipple inverted and a palpable mass in the upper outer quadrant of the right breast, measuring approximately 5 cm × 4 cm. The mass was firm with indistinct borders, relatively regular morphology, poor mobility, and no tenderness. Outpatient color Doppler ultrasound revealed heterogeneous echogenicity in the right breast, classified as Breast Imaging Reporting and Data System (BI-RADS) category 0, along with multiple ductal dilatations. The left breast exhibited a hypoechoic area (BI-RADS 3), indicative of proliferative changes. Radiographic mammography confirmed diffuse changes in the right breast (BI-RADS 0) and proliferative signs in the left breast (BI-RADS 2). Biopsy results reveal significant atypical ductal hyperplasia consistent with intermediate-grade ductal carcinoma in situ. This patient was diagnosed as ductal carcinoma in situ of the right breast (cTisN0M0 and Stage 0), accompanied by a left breast mass. INTERVENTIONS: On May 15, 2023, the patient was readmitted for further surgical intervention. Following relevant auxiliary examinations, the patient underwent nipple-areola complex-sparing radical mastectomy for the right breast, sentinel lymph node biopsy in the right axillary area, prosthesis-based breast reconstruction for the right breast, and microrotatotomy of the left breast mass on the left side on May 17. OUTCOMES: The patient made a successful recovery under scrupulous perioperative supervision and was discharged 7 days post-surgery. LESSONS: The axillary approach for endoscopic mammary gland excision and immediate implant reconstruction permits patients to preserve the esthetics of the female form while undergoing conventional medical treatment. This methodology considerably enhances the psychophysical health of the patients, thereby marking it as an advantageous practice worthy of broad dissemination in the medical community.

Ultrafast MRI and T1 and T2 Radiomics for Predicting Invasive Components in Ductal Carcinoma in Situ Diagnosed With Percutaneous Needle Biopsy.

OBJECTIVE: This study aimed to investigate the feasibility of ultrafast magnetic resonance imaging (MRI) and radiomic features derived from breast MRI for predicting the upstaging of ductal carcinoma in situ (DCIS) diagnosed using percutaneous needle biopsy. MATERIALS AND METHODS: Between August 2018 and June 2020, 95 patients with 98 DCIS lesions who underwent preoperative breast MRI, including an ultrafast sequence, and subsequent surgery were included. Four ultrafast MRI parameters were analyzed: time-to-enhancement, maximum slope (MS), area under the curve for 60 s after enhancement, and time-to-peak enhancement. One hundred and seven radiomic features were extracted for the whole tumor on the first post-contrast T1WI and T2WI using PyRadiomics. Clinicopathological characteristics, ultrafast MRI findings, and radiomic features were compared between the pure DCIS and DCIS with invasion groups. Prediction models, incorporating clinicopathological, ultrafast MRI, and radiomic features, were developed. Receiver operating characteristic curve analysis and area under the curve (AUC) were used to evaluate model performance in distinguishing between the two groups using leave-one-out cross-validation. RESULTS: Thirty-six of the 98 lesions (36.7%) were confirmed to have invasive components after surgery. Compared to the pure DCIS group, the DCIS with invasion group had a higher nuclear grade (P < 0.001), larger mean lesion size (P = 0.038), larger mean MS (P = 0.002), and different radiomic-related characteristics, including a more extensive tumor volume; higher maximum gray-level intensity; coarser, more complex, and heterogeneous texture; and a greater concentration of high gray-level intensity. No significant differences in AUCs were found between the model incorporating nuclear grade and lesion size (0.687) and the models integrating additional ultrafast MRI and radiomic features (0.680-0.732). CONCLUSION: High nuclear grade, larger lesion size, larger MS, and multiple radiomic features were associated with DCIS upstaging. However, the addition of MS and radiomic features to the prediction model did not significantly improve the prediction performance.

A clinical radiomics nomogram preoperatively to predict ductal carcinoma in situ with microinvasion in women with biopsy-confirmed ductal carcinoma in situ: a preliminary study.

PURPOSE: To predict ductal carcinoma in situ with microinvasion (DCISMI) based on clinicopathologic, conventional breast magnetic resonance imaging (MRI), and dynamic contrast enhanced MRI (DCE-MRI) radiomics signatures in women with biopsy-confirmed ductal carcinoma in situ (DCIS). METHODS: Eighty-six women with eighty-seven biopsy-proven DCIS who underwent preoperative MRI and underwent surgery were retrospectively identified. Clinicopathologic, conventional MRI, DCE-MRI radiomics, combine (based on conventional MRI and DCE-MRI radiomics), traditional (based on clinicopathologic and conventional MRI) and mixed (based on clinicopathologic, conventional MRI and DCE-MRI radiomics) models were constructed by logistic regression (LR) with a 3-fold cross-validation, all evaluated using receiver operating characteristic (ROC) curve analysis. A clinical radiomics nomogram was then built by incorporating the Radiomics score, significant clinicopathologic and conventional MRI features of mixed model. RESULTS: The area under the curves (AUCs) of clinicopathologic, conventional MRI, DCE-MRI radiomics, traditional, combine, and mixed model were 0.76 (95% confidence interval [CI] 0.59-0.94), 0.77 (95%CI 0.59-0.95), 0.74 (95%CI 0.55-0.93), 0.87 (95%CI 0.73-1), 0.8 (95%CI 0.63-0.96), and 0.93 (95%CI 0.84-1) in the validation cohort, respectively. The clinical radiomics nomogram based on mixed model showed higher AUCs than both clinicopathologic and DCE-MRI radiomics models in training/test (all P < 0.05) set and showed the greatest overall net benefit for upstaging according to decision curve analysis (DCA). CONCLUSION: A nomogram constructed by combining clinicopathologic, conventional MRI features and DCE-MRI radiomics signatures may be useful in predicting DCISMI from DICS preoperatively.

Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging.

Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland.

The imaging appearances of non-calcified ductal carcinoma in situ: A pictorial essay.

Non-calcified ductal carcinoma in situ (NCDCIS) presents as a heterogeneous entity on various imaging modalities, most frequently presenting symptomatically as a palpable lump. The combination of multiple modalities and knowledge of its potential radiological appearances are important in minimising misdiagnosis. Compared to conventional 2D mammography, both sonography and digital breast tomosynthesis show higher diagnostic accuracy in the detection of NCDCIS. Newer modalities of contrast-enhanced digital mammography and MRI have limited data at present, but early results indicate greater sensitivity for the detection of lesions that may be occult on ultrasound or mammography. Here, we present an illustrative study highlighting the varied appearances of NCDCIS on several imaging modalities including a brief review of the literature.

Non-progressive breast carcinomas detected at mammography screening: a population study.

BACKGROUND: Some breast carcinomas detected at screening, especially ductal carcinoma in situ, may have limited potential for progression to symptomatic disease. To determine non-progression is a challenge, but if all screening-detected breast tumors eventually reach a clinical stage, the cumulative incidence at a reasonably high age would be similar for women with or without screening, conditional on the women being alive. METHODS: Using high-quality population data with 24 years of follow-up from the gradually introduced BreastScreen Norway program, we studied whether all breast carcinomas detected at mammography screening 50-69 years of age would progress to clinical symptoms within 85 years of age. First, we estimated the incidence rates of breast carcinomas by age in scenarios with or without screening, based on an extended age-period-cohort incidence model. Next, we estimated the frequency of non-progressive tumors among screening-detected cases, by calculating the difference in the cumulative rate of breast carcinomas between the screening and non-screening scenarios at 85 years of age. RESULTS: Among women who attended BreastScreen Norway from the age of 50 to 69 years, we estimated that 1.1% of the participants were diagnosed with a breast carcinoma without the potential to progress to symptomatic disease by 85 years of age. This proportion of potentially non-progressive tumors corresponded to 15.7% [95% CI 3.3, 27.1] of breast carcinomas detected at screening. CONCLUSIONS: Our findings suggest that nearly one in six breast carcinomas detected at screening may be non-progressive.

Active Surveillance of Atypical Ductal Hyperplasia of the Breast.

BACKGROUND: When needle core biopsy (NCB) of the breast yields atypical ductal hyperplasia (ADH), excision is typically recommended. The natural history of ADH undergoing active surveillance (AS) is not well described. We investigate the rates of upgrade to malignancy of excised ADH and the rates of radiographic progression under AS. MATERIALS AND METHODS: We retrospectively reviewed records of 220 cases of ADH on NCB. Of patients who had surgery within 6 months of NCB, we examined the malignancy upgrade rate. In the AS cohort, we examined rates of radiographic progression on interval imaging. RESULTS: The malignancy upgrade rate among patients who underwent immediate excision (n = 185) was 15.7%: 14.1% (n = 26) ductal carcinoma in situ (DCIS) and 1.6% (n = 3) invasive ductal carcinoma (IDC). Upgrade to malignancy was less common in lesions <4 mm in size (0%) or with focal ADH (5%), and more common among lesions presenting with a radiographic mass (26%). Among the 35 patients who underwent AS, median follow-up was 20 months. Two lesions progressed on imaging (incidence 3.8% at 2 years). One patient without radiographic progression was found to have IDC at delayed surgery. The remaining lesions remained stable (46%), decreased in size (11%), or resolved (37%). CONCLUSIONS: Our findings suggest that AS is a safe approach to managing ADH on NCB for most patients. This could spare many patients with ADH from unnecessary surgery. Given that AS is being investigated for low-risk DCIS in multiple international prospective trials, these results suggest that AS should also be investigated for ADH.

Features of ductal carcinoma in situ ultrasound images.

Ultrasound images of ductal carcinoma in situ (DCIS) show a wide range of variations from mass to non-mass lesions. This article describes the characteristics of ultrasound images of DCIS based on the BC-02 study conducted by The Japanese Association of Breast and Thyroid Sonology (JABTS). In the BC-02 study, ultrasound images of 705 DCIS cases were classified by imaging findings. The results showed that non-mass abnormalities accounted for 60% of all lesions and masses for 40%. Looking at each subclassification, hypoechoic areas in the mammary gland were the most common (50% of the total), followed by solid masses (31%), mixed masses (9%), and abnormalities of the ducts (8%). These four classifications accounted for 98% of the total. Echogenic foci without a hypoechoic area, architectural distortion, and clustered microcysts were very rare, accounting for about 1% of the total. The ultrasound images of DCIS were characterized by a wide range of variations from masses to non-masses abnormalities, with hypoechoic areas in the mammary gland being the most common, followed by solid masses.

Computer Vision Identifies Recurrent and Nonrecurrent Ductal Carcinoma in Situ Lesions with Special Emphasis on African-American Women.

Although nonrecurrent and recurrent forms of ductal carcinoma in situ (DCIS) of the breast are observed, no evidence-based test can make this distinction. The current retrospective case-control study used archival DCIS samples stained with anti-phospho-Ser226-glucose transporter type 1 and anti-phosphofructokinase type L antibodies. Immunofluorescence micrographs were used to create machine learning models of recurrent and nonrecurrent biomarker patterns, which were evaluated in cross-validation studies. Clinical performance was assessed by holdout studies using patients whose data were not used in training. Micrographs were stratified according to the recurrence probability of each image. Recurrent patients were defined by at least one image with a probability of recurrence ≥98%, whereas nonrecurrent patients had none. These studies found no false-negatives, identified true-positives, and uniquely identified true-negatives. Roughly 20% of the microscope fields of recurrent lesions were computationally recurrent. Strong prognostic results were obtained for both white and African-American women. This machine tool provides the first means to accurately predict recurrent and nonrecurrent patient outcomes. Data indicate that at least some false-positive findings were true-positive findings that benefited from surgical intervention. The intracellular locations of phospho-Ser226-glucose transporter type 1 and phosphofructokinase type L likely participate in cancer recurrences by accelerating glucose flux, a key feature of the Warburg effect.

Microcalcification crystallography as a potential marker of DCIS recurrence.

Ductal carcinoma in-situ (DCIS) accounts for 20-25% of all new breast cancer diagnoses. DCIS has an uncertain risk of progression to invasive breast cancer and a lack of predictive biomarkers may result in relatively high levels (~ 75%) of overtreatment. To identify unique prognostic biomarkers of invasive progression, crystallographic and chemical features of DCIS microcalcifications have been explored. Samples from patients with at least 5-years of follow up and no known recurrence (174 calcifications in 67 patients) or ipsilateral invasive breast cancer recurrence (179 microcalcifications in 57 patients) were studied. Significant differences were noted between the two groups including whitlockite relative mass, hydroxyapatite and whitlockite crystal maturity and, elementally, sodium to calcium ion ratio. A preliminary predictive model for DCIS to invasive cancer progression was developed from these parameters with an AUC of 0.797. These results provide insights into the differing DCIS tissue microenvironments, and how these impact microcalcification formation.

Pathologic Tumor Size versus Mammography, Sonography, and MRI in Breast Cancer Based on Pathologic Subtypes.

INTRODUCTION: The standard of care for imaging of breast pathology has historically been mammography and sonography. MRI is a modern adjunct in the surgeon's toolkit. We looked to examine the differences in imaging modalities and their ability to predict the size in relation to the pathologic size after excision with focus on pathologic subtypes. METHODS: We analyzed patient records across a 4-year period from 2017 to 2021 who were treated surgically for breast cancer at our facility. We used a retrospective chart review to collect measurements that were recorded of the tumors by the radiologist for available mammography, ultrasound, and MRI which were compared to pathology report measurements of the final specimens. We subdivided the results by pathologic subtypes including invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and ductal carcinoma in situ (DCIS). RESULTS: 658 total patients met criteria for analysis. Mammography overestimated specimens with DCIS by 1.93 mm (P = .15), US underestimated by .56 (.55), and MRI overestimated by 5.77 mm (P < .01). There was no statistically significant difference in any modalities with IDC. With specimens of ILC, all 3 imaging modalities underestimated tumor size, with only US being significant. DISCUSSION: Mammography and MRI consistently overestimated tumor size with the exception of ILC while US underestimated tumor size on all pathologic subtypes. MRI significantly overestimated tumor size in DCIS by 5.77 mm. Mammography was the most accurate imaging modality for all pathologic subtypes and never had a statistically significant difference from actual tumor size.

An MRI-Based Radiomics Nomogram to Distinguish Ductal Carcinoma In Situ with Microinvasion From Ductal Carcinoma In Situ of Breast Cancer.

RATIONALE AND OBJECTIVES: Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization and individualized risk assessment. The present study aims to build and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that could distinguish DCISM from pure DCIS breast cancer. MATERIALS AND METHODS: MR images of 140 patients obtained between March 2019 and November 2022 at our institution were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The independent clinical risk factors were selected by multivariate logistic regression to establish the clinical model. The optimal radiomics features were chosen by the least absolute shrinkage and selection operator, and a radiomics signature was built. The nomogram model was constructed by integrating the radiomics signature and independent risk factors. The discrimination efficacy of our nomogram was assessed by using calibration and decision curves. RESULTS: Six features were selected to construct the radiomics signature for distinguishing DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval [CI], 0.703-0.926, 0.848-0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672-0.989, 0.764-0.999) sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544-0.801, 0.527-0.907). The decision curve also demonstrated that the nomogram model exhibited good clinical utility. CONCLUSION: The proposed noninvasive MRI-based radiomics nomogram model showed good performance in distinguishing DCISM from DCIS.

Correlation of Mammographic Microcalcifications with Final Surgical Pathology After Neoadjuvant Chemotherapy for Breast Cancer.

INTRODUCTION: Imaging guidelines for post-neoadjuvant chemotherapy (NAC) breast cancer patients lack specificity on appropriateness and utility of individual modalities for surgical planning. Microcalcifications confound mammographic interpretation. We examined the correlation between the mammographic extent of microcalcifications present post-NAC, corresponding magnetic resonance imaging (MRI) lesions, and definitive surgical pathology. METHODS: In this retrospective cohort study, patients with calcifications on mammography were collected from a database of consecutive breast cancer patients receiving NAC. The primary objective was to determine the correlation between maximum dimension of post-NAC calcifications with surgical pathology (invasive disease, tumor bed, and ductal carcinoma in situ [DCIS]), stratified by tumor receptor subgroup. Secondarily, we examined the correlation of residual disease with MRI mass enhancement (ME) and non-ME (NME). Pearson's correlation coefficient was used to evaluate statistical significance (strong: R2 ≥70%; moderate: R2=25-70%; weak: R2 ≤25%). RESULTS: Overall, 186 patients met the inclusion criteria. Mammographic calcifications correlated poorly with invasive disease (R2 = 10.8%), overestimating by 57%. In patients with calcifications on mammography, MRI ME and NME correlated weakly with the maximum dimension of invasive disease and DCIS. In triple-negative breast cancer (TNBC) patients, invasive disease correlated strongly with the maximum dimension of calcifications (R2 = 83%) and moderately with ME (R2 = 37.7%) and NME (R2 = 28.4%). CONCLUSION: Overall, current imaging techniques correlate poorly and overestimate final surgical pathology. This poor correlation may lead to uncertainty in the extent of required surgical excision and the exclusion of potential candidates for non-surgical management in ongoing trials. TNBCs would be good candidates for these trials given the stronger observed correlations between pathology and imaging.

Management of Lobular Neoplasia Diagnosed by Core Biopsy.

Lobular neoplasia (LN) involves proliferative changes within the breast lobules. LN is divided into lobular carcinoma in situ (LCIS) and atypical lobular hyperplasia (ALH). LCIS can be further subdivided into three subtypes: classic LCIS, pleomorphic LCIS, and LCIS with necrosis (florid type). Because classic LCIS is now considered as a benign etiology, current guidelines recommend close follow-up with imaging versus surgical excision. The goal of our study was to determine if the diagnosis of classic LN on core needle biopsy (CNB) merits surgical excision. This is a retrospective, observational study conducted at Mount Auburn Hospital, Cambridge, MA, from May 17, 2017, through June 30, 2020. We reviewed the data of breast biopsies conducted at our hospital over this period and included patients who were diagnosed with classic LN (LCIS and/or ALH) and excluded patients having any other atypical lesions on CNB. All known cancer patients were excluded. Of the 2707 CNBs performed during the study period, we identified 68 women who were diagnosed with ALH or LCIS on CNB. CNB was performed for an abnormal mammogram in the majority of patients (60; 88%) while 7(10.3%) had an abnormal breast magnetic resonance imaging study (MRI), and 1 had an abnormal ultrasound (US). A total of 58 patients (85%) underwent excisional biopsy, of which 3 (5.2%) showed malignancy, including 2 cases of DCIS and 1 invasive carcinoma. In addition, there was 1 case (1.7%) with pleomorphic LCIS and 11 cases with ADH (15.5%). The management of LN found on core biopsy is evolving, with some advocating surgical excision and others recommending observation. Our data show a change in diagnosis with excisional biopsy in 13 (22.4%) of patients with 2 cases of DCIS, 1 invasive carcinoma, 1 pleomorphic LCIS, and 9 cases of ADH, diagnosed on excisional biopsy. While ALH and classic LCIS are considered benign, the choice of ongoing surveillance versus excisional biopsy should be made with shared decision making with the patient, with consideration of personal and family history, as well as patient preferences.