Detection of invasive ductal carcinoma in quadrant breast areas by electrical impedance tomography implemented with gaussian relaxation-time distribution (EIT-GRTD).

Invasive ductal carcinoma (IDC) in breast specimens has been detected in the quadrant breast area: (I) upper outer, (II) upper inner, (III) lower inner, and (IV) lower outer areas by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). The EIT-GRTD consists of two steps which are (1) the optimum frequencyfoptselection and (2) the time constant enhancement of breast imaging reconstruction.foptis characterized by a peak in the majority measurement pair of the relaxation-time distribution functionγ,which indicates the presence of IDC.γrepresents the inverse of conductivity and indicates the response of breast tissues to electrical currents across varying frequencies based on the Voigt circuit model. The EIT-GRTD is quantitatively evaluated by multi-physics simulations using a hemisphere container of mimic breast, consisting of IDC and adipose tissues as normal breast tissue under one condition with known IDC in quadrant breast area II. The simulation results show that EIT-GRTD is able to detect the IDC in four layers atfopt= 30, 170 Hz. EIT-GRTD is applied in the real breast by employed six mastectomy specimens from IDC patients. The placement of the mastectomy specimens in a hemisphere container is an important factor in the success of quadrant breast area reconstruction. In order to perform the evaluation, EIT-GRTD reconstruction images are compared to the CT scan images. The experimental results demonstrate that EIS-GRTD exhibits proficiency in the detection of the IDC in quadrant breast areas while compared qualitatively to CT scan images.

A Nomogram Based on Conventional Ultrasound Radiomics for Differentiating Between Radial Scar and Invasive Ductal Carcinoma of the Breast.

ABSTRACT: We aimed to develop and validate a nomogram based on conventional ultrasound (CUS) radiomics model to differentiate radial scar (RS) from invasive ductal carcinoma (IDC) of the breast. In total, 208 patients with histopathologically diagnosed RS or IDC of the breast were enrolled. They were randomly divided in a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 63). Overall, 1316 radiomics features were extracted from CUS images. Then a radiomics score was constructed by filtering unstable features and using the maximum relevance minimum redundancy algorithm and the least absolute shrinkage and selection operator logistic regression algorithm. Two models were developed using data from the training cohort: one using clinical and CUS characteristics (Clin + CUS model) and one using clinical information, CUS characteristics, and the radiomics score (radiomics model). The usefulness of nomogram was assessed based on their differentiating ability and clinical utility. Nine features from CUS images were used to build the radiomics score. The radiomics nomogram showed a favorable predictive value for differentiating RS from IDC, with areas under the curve of 0.953 and 0.922 for the training and validation cohorts, respectively. Decision curve analysis indicated that this model outperformed the Clin + CUS model and the radiomics score in terms of clinical usefulness. The results of this study may provide a novel method for noninvasively distinguish RS from IDC.

Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma.

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

B3 Breast Lesions: Positive Predictive Value and Follow-Up on a Large Single-Institution Series.

INTRODUCTION: Lesions of uncertain malignant potential (B3) represent 10% of core needle biopsies (CNBs) or vacuum-assisted breast biopsies (VABBs). Traditionally, B3 lesions are operated on. This study investigated the association between B3 subtypes and malignancy to determine the best management. METHODS: Pre- and postoperative histological reports from 226 patients, who had undergone excisional surgery for B3 lesions, following CNB or VABB, were retrospectively analyzed. The correlation between the CNB/VABB diagnosis and the final pathology was investigated, along with the correlation between malignancy upgrade and the type of mammographic lesion. The positive predictive value (PPV) of malignancy of B3 lesions was calculated by simple logistic regression. Patients without cancer diagnosis underwent a 7-y follow-up. RESULTS: Pathology showed 171 (75.6%) benign and 55 (24.3%) malignant lesions. The PPV was 24.3% (P = 0.043), including 31 (13.7%) ductal carcinomas in situ and 24 (10.6%) invasive carcinomas. The most frequently upgraded lesions were atypical ductal hyperplasia, 34.2% (P = 0.004), followed by lobular intraepithelial neoplasia, 27.5% (P = 0.025). The median diameter of mammographic lesions was 1.5 [0.9-2.5] cm, while for surgical specimens, it was 5 [4-7] cm (P < 0.0001). Mammographic findings and histology showed a significant correlation (P = 0.038). After a 7-y follow-up, 15 (8.9%) patients developed carcinoma, and 7 patients (4%) developed a new B3 lesion. CONCLUSIONS: We can conclude that atypical ductal hyperplasia and lobular intraepithelial neoplasia still require surgery for a significant PPV. Other types that lacked significance or confidence intervals were too wide to draw any conclusion.

Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients.

An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.

Pre-surgical cryoablation in ≤ 2 cm ER + /HER2-tumors. Prognostic factors for the presence of residual invasive carcinoma.

BACKGROUND: Breast cancer remains the most commonly diagnosed cancer in women. Breast-conserving surgery (BCS) is the standard approach for small low-risk tumors. If the efficacy of cryoablation is demonstrated, it could provide a minimally invasive alternative to surgery. PURPOSE: To determine the success of ultrasound-guided cryoablation in achieving the absence of Residual Invasive Cancer (RIC) for patients with ER + /HER2- tumors ≤ 2cm and sonographically negative axillary nodes. MATERIALS AND METHODS: This prospective study was carried out from April 2021 to June 2023, and involved 60 preoperative cryoablation procedures on ultrasound-visible, node-negative (cN0) infiltrating ductal carcinomas (IDC). Standard diagnostic imaging included mammography and tomosynthesis, supplemented by ultrasound-guided biopsy. MRI was performed in patients with associated intraductal carcinoma (DCIS) and an invasive component on core needle biopsy (18 out of 22 cases). All tumors were tagged with ferromagnetic seeds. A triple-phase protocol (freezing-thawing-freezing) with Argon was used, with an average procedure duration of 40 min. A logistic regression model was applied to determine significant correlation between RIC and the study variables. RESULTS: Fifty-nine women (mean age 63 ± 8 years) with sixty low-risk unifocal IDC underwent cryoablation prior to surgery. Pathological examination of lumpectomy specimens post-cryoablation revealed RIC in only one of 38 patients with pure IDC and in 4 of 22 mixed IDC/DCIS cases. All treated tumors had clear surgical margins, with no significant procedural complications. CONCLUSIONS: Cryoablation was effective in eradicating 97% of pure infiltrating ER + /HER2-tumors ≤ 2cm, demonstrating its potential as a surgical alternative in selected patients.

Characterization and classification of ductal carcinoma tissue using four channel based stokes-mueller polarimetry and machine learning.

Interaction of polarized light with healthy and abnormal regions of tissue reveals structural information associated with its pathological condition. Even a slight variation in structural alignment can induce a change in polarization property, which can play a crucial role in the early detection of abnormal tissue morphology. We propose a transmission-based Stokes-Mueller microscope for quantitative analysis of the microstructural properties of the tissue specimen. The Stokes-Mueller based polarization microscopy provides significant structural information of tissue through various polarization parameters such as degree of polarization (DOP), degree of linear polarization (DOLP), and degree of circular polarization (DOCP), anisotropy (r) and Mueller decomposition parameters such as diattenuation, retardance and depolarization. Further, by applying a suitable image processing technique such as Machine learning (ML) output images were analysed effectively. The support vector machine image classification model achieved 95.78% validation accuracy and 94.81% testing accuracy with polarization parameter dataset. The study's findings demonstrate the potential of Stokes-Mueller polarimetry in tissue characterization and diagnosis, providing a valuable tool for biomedical applications.

Comparison of Clinical Breast Exam to Breast MRI Surveillance in Patients Following Nipple-Sparing Mastectomy.

BACKGROUND: Nipple sparing mastectomy (NSM) is increasingly being performed for patients with breast cancer. However, optimal postoperative surveillance has not been defined. METHODS: A prospectively maintained database identified patients with in-situ and invasive cancer who underwent NSM between 2007-2021. Clinical data on postoperative breast surveillance and interventions were collected. Patients who had MRI surveillance versus clinical breast exam (CBE) alone were compared with respect to tumor characteristics, recurrence, and survival. RESULTS: A total of 483 NSMs were performed on 399 patients. 255 (63.9%) patients had invasive ductal carcinoma, 31 (7.8%) invasive lobular carcinoma, 92 (23.1%) DCIS, 6 (1.5%) mixed ductal and lobular carcinoma, 9 (2.3%) others, and 6 (1.5%) unknown. Postoperatively, 265 (66.4%) patients were followed with CBE alone and 134 (33.6%) had surveillance MRIs. At a median follow-up of 33 months, 20 patients (5.0%) developed in-breast recurrence, 6 patients had (1.5%) an axillary recurrence, and 28 with (7.0%) distant recurrence. 14 (53.8%) LRR were detected in the CBE group and 12 (46.2%) were detected in the MRI group (P = .16). Overall survival (OS) was 99%, with no difference in OS between patients who had CBE alone versus MRI (P = .46). MRI was associated with higher biopsy rates compared to CBE alone (15.8% vs. 7.8%, P = .01). CONCLUSIONS: Compared to CBE alone, the use of screening MRI following NSM results in higher rate of biopsy and no difference in overall survival.

Intra and peritumoral PET radiomics analysis to predict the pathological response in breast cancer patients receiving neoadjuvant chemotherapy.

OBJECTIVE: The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). METHODS: Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-T) was manually segmented, then a voxel-thick VOI was added around VOI-T to define the peritumoral area (VOI-PT). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A "radiomic model" was created with only radiomic features, and a "patho-radiomic model" was created using radiomic features and immunohistochemical data. RESULTS: Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-T (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-PT; no correlation was found between Morphological_Compacity-PT and NGTDM_contrast-PT. The obtained radiomic model's sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903). CONCLUSIONS: Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.

Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast.

BACKGROUND: Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. OBJECTIVE: To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. METHODS: The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. RESULTS: The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. CONCLUSION: ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.

Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study.

OBJECTIVES: To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. METHODS: This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). RESULTS: The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort. CONCLUSIONS: Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. CLINICAL RELEVANCE STATEMENT: By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. KEY POINTS: • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

Could breast multiparametric MRI discriminate between pure ductal carcinoma in situ and microinvasive carcinoma?

BACKGROUND: Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis. PURPOSE: To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC). MATERIAL AND METHODS: Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome. RESULTS: Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size (P <0.001), clustered ring enhancement (P <0.001), segmental distribution (P <0.001), diffusion restriction (P = 0.005), and ADC values <1.3 × 10-3 mm2/s (P = 0.004). CONCLUSION: Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning.

The Effect of Intraoperative Margin Assessment During Breast Conserving Surgery for Breast Cancer in a Dutch Cohort.

INTRODUCTION: Intraoperative specimen radiography is a routinely used procedure to ensure adequate resection of non-palpable breast tumors. Intraoperative digital specimen mammography (IDSM) is an alternative to conventional specimen radiography (CSR) which provides immediate specimen evaluation and can potentially decrease operation time. IDSM may also result in lower positive margin and re-excision rates. IDSM was implemented in our hospital in 2018. The objective of this study was to evaluate the effect of using IDSM versus CSR on operation time, margin status and re-excision rates in breast conserving surgery. METHODS: The present study is a single-center retrospective cohort study with 2 patient cohorts: one which underwent CSR (n = 532) and one which underwent IDSM (n = 475). The primary outcome was the operation time. Secondary outcomes were the margin status of the primary surgery, the cavity shaving rate, and the re-excision rate. Differences between cohorts were compared using univariate statistics and multiple regression analyses to adjust for variables that were significantly different between the groups. RESULTS: IDSM use was associated with an 8-minute reduction in surgery time (B = -8.034, 95% CI [-11.6, -4.5]; P < .001). Treatment variables independently associated with the operation time included use of IDSM, type of surgery, and performance of cavity shaving. Cavity shaves were more often performed when IDSM was used (24% for IDSM vs. 14% for CSR, P < .001), while the proportion of negative margin rates (93% for IDSM vs. 96% for CSR, P = .070) was comparable. CONCLUSION: IDSM was associated with a modest reduction in operation time. Surgeons performed more cavity shaves since the introduction of IDSM, but this increase was not reflected by difference in negative margin rates.

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.

Digital mammography and digital breast tomosynthesis for detecting invasive lobular and ductal carcinoma.

PURPOSE: Invasive lobular carcinoma (ILC) is a distinct histological subtype of breast cancer that can make early detection with mammography challenging. We compared imaging performance of digital breast tomosynthesis (DBT) to digital mammography (DM) for diagnoses of ILC, invasive ductal carcinoma (IDC), and invasive mixed carcinoma (IMC) in a screening population. METHODS: We included screening exams (DM; n = 1,715,249 or DBT; n = 414,793) from 2011 to 2018 among 839,801 women in the Breast Cancer Surveillance Consortium. Examinations were followed for one year to ascertain incident ILC, IDC, or IMC. We measured cancer detection rate (CDR) and interval invasive cancer rate/1000 screening examinations for each histological subtype and stratified by breast density and modality. We calculated relative risk (RR) for DM vs. DBT using log-binomial models to adjust for the propensity of receiving DBT vs. DM. RESULTS: Unadjusted CDR per 1000 mammograms of ILC overall was 0.33 (95%CI: 0.30-0.36) for DM; 0.45 (95%CI: 0.39-0.52) for DBT, and for women with dense breasts- 0.33 (95%CI: 0.29-0.37) for DM and 0.54 (95%CI: 0.43-0.66) for DBT. Similar results were noted for IDC and IMC. Adjusted models showed a significantly increased RR for cancer detection with DBT compared to DM among women with dense breasts for all three histologies (RR; 95%CI: ILC 1.53; 1.09-2.14, IDC 1.21; 1.02-1.44, IMC 1.76; 1.30-2.38), but no significant increase among women with non-dense breasts. CONCLUSION: DBT was associated with higher CDR for ILC, IDC, and IMC for women with dense breasts. Early detection of ILC with DBT may improve outcomes for this distinct clinical entity.

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.

Effectiveness of Carbon Localization for Invasive Breast Cancer: An Institutional Experience.

Introduction: The final oncological and aesthetic results of breast-conserving surgery (BCS) are influenced by the precise localization of breast cancer (BC) tumors and by the quality of the intraoperative margin assessment technique. This study aimed to assess the effectiveness of the carbon localization (CL) technique by determining the success rate of BC identification and the proportion of adequate complete resection of BC lesions. Methods: We conducted a cross-sectional retrospective study of patients treated with primary BCS for invasive BC who underwent CL of their BC lesion at the Jules Bordet Institute between January 2015 and December 2017. Descriptive statistics with categorical and continuous variables were used. The success rate of tumor identification and the rate of adequate excision were calculated using the test of percentages for independent dichotomous data. Results: This study included 542 patients with 564 nonpalpable BC lesions. The median pathological tumor size was 12 mm. Of these, 460 were invasive ductal carcinomas. Most of the tumors were of the luminal subtype. CL was performed using ultrasound guidance in 98.5% of cases. The median delay between CL and surgery was 5 days, with 46% of the patients having CL one day before surgery. The lumpectomy weighed 38 g on average, with a median diameter of the surgical sample at 6 cm and a median volume of 44 cm3 (6-369). One-stage complete resection was successfully performed in 93.4% of cases. In 36% of cases, an intraoperative re-excision was performed, based on intraoperative macroscopic pathological margin evaluation. The tumor was identified in 98.9% of cases in the breast surgical specimen. Conclusion: This study demonstrated high success rates for BC tumor identification (99%) and one-stage complete resection (93.4%) after BCS and CL. These results show that CL is an effective, simple, and inexpensive localization technique for successful excision of BC lesions during BCS.

Prediction of nipple involvement in breast cancer after neoadjuvant chemotherapy: Should we rely on breast MRI to preserve the nipple?

BACKGROUND: Indications for nipple sparing mastectomy (NSM) is extending to post-neoadjuvant chemotherapy (NAC) setting. Eligibility for NSM with an optimum tumor-nipple distance (TND) after NAC is unclear. We examined predictive factors for nipple tumor involvement in patients undergoing total mastectomy following NAC. METHODS: Clinical and pathological data from prospectively collected medical records of women with invasive breast carcinoma, who were undergone NAC and total mastectomy with sentinel lymph node biopsy and/or axillary lymph node dissection were analyzed. PreNAC and postNAC magnetic resonance imaging (MRI) views were examined and a cut-off TND value for predicting the negative nipple tumor status was determined. RESULTS: Among 180 women, the final mastectomy specimen analysis revealed that 12 (7%) had nipple involvement as invasive carcinoma. Patients with nipple involvement had more postNAC multifocal/multicentric tumors (p: 0.03), larger tumors on preNAC and postNAC images (p: 0.002 and p < 0.001), shorter median TNDs on preNAC and postNAC images (7 mm-IQR 1.5-14, p: 0.005 and 8.5 mm-IQR 3-15.5, p < 0.001, respectively), more nipple retraction on preNAC and postNAC images (p: 0.007 and p: 0.006) and more nipple areola complex skin thickening (> 2mm) on preNAC and postNAC images (p < 0.001 and p: 0.01). The best likelihood ratios (LR) belonged to the postNAC positivity of the < 20 mm TND, with a + LR of 3.40, and - LR of 0.11 for nipple involvement. PreNAC positivity of the < 20 mm TND also had a similar - LR of 0.14. CONCLUSION: A TND-cut-off  ≥ 2 cm on preNAC and postNAC MRI was shown to be highly predictive of negative nipple tumor involvement.