Breast Tumor Location Research Articles

Enhanced breast tumor localization with DRA antenna backscattering and GPR algorithm in microwave imaging

With the rapid rate of increasing breast cancer cases across the globe and restrictions of existing early stage detection techniques, microwave imaging based cancer and malignant cell diagnosing methodology is finding its way. Microwave imaging technology has higher accuracy of detection in the variation of tissue properties. In this article, a dielectric resonator based wide band antenna is designed and investigated for microwave imaging (MWI) of early breast cancer detection due to its improved accuracy. The designed antenna has compact size, broad frequency spectrum, high gain and broadside radiation characteristics. To attain the wider bandwidth from (6.5 GHz–12.5 GHz), two rectangular shaped dielectric slabs are used. Wide band microwave frequency band supports the high resolution images. A C-shaped defected ground structure is used to tune the impedance matching. Dielectric resonator based antenna has higher gain and reduces the squints in antenna impedance. Two-asymmetrical dielectric slabs contribute in the excitation of different resonating modes which in turn enhance the bandwidth. It also contributes to excite multiple resonance mode that in turn enhance the bandwidth. Antenna performance is first numerically and experimentally verified in free space. Then, simulation based performance is examined for the microwave imaging. Numerical phantom with equivalent tissue properties is designed with and without the tumor. Unhealthy cells have higher water percentage and larger dielectric constant as compared to healthy cells. It causes the different in the back scattered signal of antenna, which is analyzed to diagnose the tumor or cancer. Here, a maximum deviation of 16 dB is observed in the backscattered signal. Furthermore, surface of the breast tissue is scanned along the x-axis and y-axis at different angels. The collected signals are used to reconstruct the microwave image of the interior. Mean value data of backscattered signals is used to locate the existence of unhealthy cells and GPR algorithm is used to calculate the depth of a 4 mm tumor in breast tissue. Proposed structure has shown efficient performance for microwave imaging.

Non-Invasive 3D Breast Tumor Localization: A Viable Alternative to Invasive Tumor Marking.

Background: We present a detailed description and the preliminary results of our original technique for non-invasive three-dimensional tumor localization in the breast, which was created as an alternative to standard invasive tumor marking before neoadjuvant systemic therapy (NAST), aiming to enable adequate surgery after complete tumor regression. Methods: A detailed description of the technique is provided in the main text. The technique's feasibility and precision were assessed in a single-arm, prospective study based on the histological parameters of the adequacy and rationality of the excision of completely regressed tumor beds. Results: Out of 94 recruited patients, 15 (16%) were deemed unsuitable, mainly due to the tumors' inadequate ultrasound visibility. Among the 79 processed patients, 31 (39%) had complete clinical regression after NAST and were operated on using our technique. The histological parameters of surgical precision (signs of tumor regression: 24/31; microscopic cancer residues: 7/31) were verified in all excised specimens (100% precision). There were no positive margins in seven cases with microscopic residues, indicating our technique's capacity to enable oncologically safe post-NAST surgery. Conclusions: The proposed technique is feasible and satisfactorily accurate in determining the location of regressed tumors, thus representing an alternative to invasive tumor marking, especially in surgical centers lacking trained staff and equipment for invasive marking. The technique's limitations are mainly related to the inadequate ultrasound visibility of the tumor.

Use of an AI-powered visualization platform to support precision clinical decision-making in patients with breast cancer undergoing neoadjuvant chemotherapy.

e12618 Background: Chemotherapy to reduce cancer burden and tumor size ("downstaging") is widely used in neoadjuvant treatment of early-stage breast cancer (ESBC) and can have a dramatic impact on selection of further treatment courses, including surgery. The resulting decision-making process between physicians and cancer patients requires detailed and effective communication of the patient's disease status, and clear presentation of this information in Tumor Board settings. Artificial intelligence (AI) -powered visualization platforms offer the potential to aid in evaluating breast tumor location and morphology, and accordingly optimize treatment. Additionally, these tools offer enhanced communication with patients regarding future treatment options, particularly serving as clinical decision support (CDS) tools. Methods: We assessed the performance of an AI platform that employs dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to render three-dimensional (3D) visualizations of breast tumors and 5 additional chest tissues on an independent validation dataset comprise of 100 ESBC cases with ground-truth tumor labels vetted by 2 breast-specialized radiologists. The platform provides 3D depictions of the tumor and its multi-tissue context, as well as functionalities for quantifying tumor morphology, tumor-to-landmark structure distances, excision volumes, and approximate surgical margins. We subsequently evaluated the tool's utility in a real-world setting to understand how the tool impacts clinical decision-making and patient communication, specifically around surgical planning in the context of neoadjuvant chemotherapy (NACT). Results: The platform generated accurate 3D tumor maps, as evidenced by a median Dice score of 75.6% and a median Hausdorff distances of 15.1 mm relative to the radiologist validation set. The automatic longest tumor dimension calculations were linearly correlated with radiologist direct measurements (Pearson correlation coefficient = 0.78) and had a median absolute difference of 6.3 mm. We found strong overlap between the platform’s ground-truth convex hulls, as shown by a median Dice score of 81.5%. When used by a skilled breast-specialized surgeon, the tool enabled rapid and intuitive patient-physician communication of the location and extent of disease, and potential degrees of downstaging to breast-conserving surgery. Conclusions: Detailed 3D tumor and surrounding multi-tissue depictions offer both qualitative and quantitative comprehension of cancer topology and may aid in formulating an optimal treatment approach for breast cancer treatment. We show that detailed depictions enhance communication between physicians and patients and may aid in streamlining decision-making.

Artificial Intelligence-Based Thermal Imaging for Breast Tumor Location and Size Estimation Using Thermal Impedance

Abstract Tumors can be detected from a temperature gradient due to high vascularization and increased metabolic activity of cancer cells. Thermal infrared images have been recognized as potential alternatives to detect these tumors. However, even the use of artificial intelligence directly on these images has failed to accurately locate and detect the tumor size due to the low sensitivity of temperatures and position within the breast. Thus, we aimed to develop techniques based on applying the thermal impedance method and artificial intelligence to determine the origin of the heat source (abnormal cancer metabolism) and its size. The low sensitivity to tiny and deep tumors is circumvented by utilizing the concept of thermal impedance and artificial intelligence techniques such as deep learning. We describe the development of a thermal model and the creation of a database based on its solution. We also outline the choice of detectable parameters in the thermal image, the use of deep learning libraries, and network training using convolutional neural networks (CNNs). Lastly, we present tumor location and size estimates based on thermographic images obtained from simulated thermal models of a breast, using Cartesian geometry and a scanned geometric shape of an anatomical phantom model.

Oncoplastic Breast Conservation for Central Tumors: Definition, Classification, and the Analysis of Single Institution Experience.

Tumors in the central part of the breast are usually considered more aggressive and technically difficult, which limits breast conservation. The definition of central tumors from a surgical point of view, classification of the techniques for partial breast reconstruction, and conceptual algorithm of choice based on tumor and breast characteristics are proposed, along with the estimation of surgical and oncological safety. This is a retrospective analysis of the single-institution experience, with a focus on the decision-making process for choosing the oncoplastic breast-conserving surgery technique. To evaluate the safety of breast conservation for central tumors, a comparative analysis of early surgical complications and oncological long-term results of treatment in patients with central breast tumor location and other breast tumor locations was performed. A total of 940 lumpectomies were performed in 926 patients during 15 years. The central breast tumor location group included 128 patients with 130 lumpectomies (13.8%), and the other breast tumor locations group included 798 patients with 810 lumpectomies (86.2%). We did not find any significant differences in the rate of early surgical complications and involved margins, local and systemic recurrence rates, time to progression, or overall survival between the groups. Oncoplastic breast-conserving surgery is a safe procedure for the treatment of central tumors. In our opinion, the proposed classification of partial breast reconstruction techniques and an algorithm of their choice allow for effective restoration of the breast shape and volume according to the parameters of the tumor, breast, surgeon, and patient preferences.

CNN-Based Approach for Non-Invasive Estimation of Breast Tumor Size and Location Using Thermographic Images

The characterization of tumors is crucial for guiding appropriate treatment strategies and enhancing patient survival rates. Surface thermography shows promise in the non-invasive detection of thermal patterns associated with the existence of breast tumors. Nevertheless, the precise prediction of both tumor size and location using temperature characteristics presents a critical challenge. This is due to the limited availability of thermal images labeled with the corresponding tumor size and location. This work proposes a deep learning approach based on convolutional neural networks (CNN) in combination with thermographic images for estimating breast tumor size and location. Successive COMSOL-based simulations are conducted, including a 3D breast model with various tumor scenarios. Thus, different noise levels were included in the development of the thermographic image dataset. Every image was accordingly labeled with the corresponding tumor location and size to train the CNN model. Mean absolute error (MAE) and the coefficient of determination (R²) were considered as evaluation metrics. The results show that the proposed CNN model achieved a reasonable prediction performance with MAE–R² values of 0.872–98.6% for tumor size, 1.161–96.8% for x location, 1.086–97.1% for y location, and 0.954–96.7% for z location. This study indicates that the combination of surface thermography and deep learning is a convenient tool for predicting breast tumor parameters.

Investigation of the risk factors in the development of radionecrosis in patients with brain metastases undergoing stereotactic radiotherapy.

To investigate the incidence, timing, and the factors predictors radionecrosis (RN) development in brain metastases (BMs) undergoing stereotactic radiotherapy (SRT). The study evaluated 245 BMs who exclusively received SRT between 2010 and 2020. RN was detected pathologically or radiologically. The median of follow-up was 22.6 months. RN was detected in 18.4% of the metastatic lesions, and 3.3% symptomatic, 15.1% asymptomatic. The median time of RN was 22.8 months (2.5-39.5), and the rates at 6, 12, and 24 months were 16.8%, 41.4%, and 66%, respectively. Univariate analysis revealed that Graded Prognostic Assessment (P = .005), Score Index of Radiosurgery (P = .015), Recursive Partitioning Analysis (P = .011), the presence of primary cancer (P = .004), and localization (P = .048) significantly increased the incidence of RN. No significant relationship between RN and brain-gross tumour volume doses, planning target volume, fractionation, dose (P > .05). Multivariate analysis identified SIR > 6 (OR: 1.30, P = .021), primary of breast tumour (OR: 2.33, P = .031) and supratentorial localization (OR: 3.64, P = .025) as risk factors. SRT is used effectively in BMs. The incidence of RN following SRT is undeniably frequent. It was observed that the incidence rate increased as the follow-up period increased. We showed that brain-GTV doses are not predictive of RN development, unlike other publications. In study, a high SIR score and supratentorial localization were identified as factors that increased the risk of RN. RN is still a common complication after SRT. Symptomatic RN is a significant cause of morbidity. The causes of RN are still not clearly identified. In many publications, brain dose and volumes have been found to be effective in RN. But, with this study, we found that brain dose volumes and fractionation did not increase the incidence of RN when brain doses were taken into account. The most important factor in the development of RN was found to be related to long survival after SRT.

Biomimetic bright optotheranostics for metastasis monitoring and multimodal image-guided breast cancer therapeutics

Nanoparticle formulations blending optical imaging contrast agents and therapeutics have been a cornerstone of preclinical theranostic applications. However, nanoparticle-based theranostics clinical translation faces challenges on reproducibility, brightness, photostability, biocompatibility, and selective tumor targeting and penetration. In this study, we integrate multimodal imaging and therapeutics within cancer cell-derived nanovesicles, leading to biomimetic bright optotheranostics for monitoring cancer metastasis. Upon NIR light irradiation, the engineered optotheranostics enables deep visualization and precise localization of metastatic lung, liver, and solid breast tumors along with solid tumor ablation. Metastatic cell-derived nanovesicles (∼80 ± 5 nm) are engineered to encapsulate imaging (emissive organic dye and gold nanoparticles) and therapeutic agents (anticancer drug doxorubicin and photothermally active organic indocyanine green dye). Systemic administration of biomimetic bright optotheranostic nanoparticles shows escape from mononuclear phagocytic clearance with (i) rapid tumor accumulation (3 h) and retention (up to 168 h), (ii) real-time monitoring of metastatic lung, liver, and solid breast tumors and (iii) 3-fold image-guided solid tumor reduction. These findings are supported by an improvement of X-ray, fluorescence, and photoacoustic signals while demonstrating a tumor reduction (201 mm3) in comparison with single therapies that includes chemotherapy (134 mm3), photodynamic therapy (72 mm3), and photothermal therapy (88mm3). The proposed innovative platform opens new avenues to improve cancer diagnosis and treatment outcomes by allowing the monitorization of cancer metastasis, allowing the precise cancer imaging, and delivering synergistic therapeutic agents at the solid tumor site.

Breast cancer localization by iodine seed 125I vs. wire- -guided localization - retrospective case-control study.

For many years, the gold standard in the localization of non-palpable malignant breast tumors has been the use of wire-guided method. However, this has recently been replaced by more modern localization techniques in many institutions. This is a retrospective case-control study comparing two localization techniques (iodine seed 125I and wire-guided localization) for localizing non-palpable tumors in patients with histologically verified breast carcinoma. The study included 62 patients - 31 with localization of malignant breast tumor by iodine seed (subgroup 125I) and 31 by wire-guided localization (subgroup FV). The average volume of the resected tissue in subgroup 125I (46.2 cm3) was statistically significantly smaller compared to subgroup FV (83.7 cm3; P = 0.0063). R0 resection was achieved in 29 cases (93.5%) in subgroup 125I and in 24 cases (77.4%) in subgroup FV (P = 0.0714). In subgroup 125I, re-resection was not indicated in any case, while in subgroup FV, re-resection due to tumor reaching the margin was indicated in 6 cases (19.4%; P = 0.01). Our initial experience show that the use of iodine seeds for localizing non-palpable breast tumors is associated with the removal of a smaller volume of resected tissue compared to wire-guided localization, with a trend towards more frequent achievement of R0 resection. In the subgroup of patients localized with iodine seeds, there was a smaller proportion of re-resections due to inadequate safety margins.

Combining Contrast-Enhanced Mammography and Radioactive-Free Magnetic Seed Localization of Non-palpable Breast Tumors: A Feasibility Study

Combining Contrast-Enhanced Mammography and Radioactive-Free Magnetic Seed Localization of Non-palpable Breast Tumors: A Feasibility Study

Advancements in the research of combining MRI and CRISPR technology for the treatment of breast cancer

Cancer remains a significant contributor to global mortality, posing a considerable public health challenge. Among the various types of cancer, breast cancer stands out as one of the most prevalent among women worldwide. Ongoing efforts are dedicated to the development of innovative treatments with minimal side effects. A groundbreaking genome technique for editing, CRISPR-associated protein-9 (Cas9), also known as clustered regularly interspaced short palindromic repeat (CRISPR), has become an effective instrument for genetic modification. This technology has revolutionized genome editing across diverse biological fields, including its applications in genome therapy and tumor research. With ongoing advancements in sensitivity, specificity, and diagnostic accuracy, magnetic resonance imaging (MRI) has gained increasing recognition for the detection of breast cancer. MRIs capability to produce high-resolution images plays a pivotal role in aiding healthcare professionals in precise localization of breast masses, tumors, and other abnormalities. Additionally, it facilitates the assessment of parameters like tumor size, extent, and lymph node involvement, contributing to the formulation of more accurate treatment strategies. Here is a review summary of the combined utilization of MRI and CRISPR technology in breast cancer treatment. It explores the synergy between these two technologies, discussing their advantages and disadvantages and their potential to enhance diagnostic and therapeutic outcomes.

Comparison between conventional and oncoplastic breast surgeries regarding resection margins and local recurrences in breast cancer: Retrospective study

Background Breast cancer is the most common cancer in women and the second-leading cause of cancer-related fatalities globally. Breast-conserving surgery (BCS) is the primary surgical treatment for early-stage breast cancer, with oncoplastic BCS techniques expanding its possibilities. BCS offers a higher quality of life compared to mastectomy. However, it has two absolute contraindications: the inability to produce negative margins without deforming the breast and inflammatory breast cancer. Oncoplastic displacement and replacement BCS allow resections of up to 50% of breast volume without creating deformity. Positive surgical margins often require a second procedure, which can increase pain, complications, and medical expenses. Patient and methods This retrospective cohort study analyzed 310 patients with primary invasive breast cancer or DCIS who underwent conventional or oncoplastic breast cancer surgery. Most patients underwent lateral and therapeutic reduction mammoplasties. Other techniques included LD flap, TDAP flap, mini-LD muscle flap, ICAP flap, grisotti flap, LICAP flap, and modified round block technique. Oncoplastic techniques were tailored based on breast cup size, tumor size, tumor location, and patient preference. Wide local excision of the tumor with free margins was confirmed through intraoperative frozen sections. Result The mean weight of the specimen was 55.89 gm in the conventional group and 101.55 gm in the oncoplastic group. The mean operative time was 65.9 min in the conventional group, while the oncoplastic group had 72.58 min Complications included seroma, hematoma, wound infection, wound dehiscence, lymphedema, and partial nipple necrosis. The aesthetic outcome was excellent, with 132 patients satisfied with their results in the oncoplastic group compared to 90 in the conventional group. Conclusion Oncoplastic breast surgeries are safe, feasible and became the standard of care in breast cancer. Oncoplastic breast surgeries are of choice in cases of multifocal cancer. People who performed oncoplastic breast surgeries had wider free margins, much more aesthetic outcome, better psychological status, less redo surgery, less late deformities, more time consuming, more wound complications improved with frequent dressing, and did not causea delay in the adjuvant therapy, more requirement for contralateral symmetrizing surgery.

Can Supine Breast Magnetic Resonance Imaging After a Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging Provide Information for Supine Procedures?

A retrospective analysis was conducted on 75 lesions in 50 patients with pathologically proven breast cancer who underwent MRI in prone and supine positions between December 2019 and December 2020. The transverse, anteroposterior, and craniocaudal dimensions (in millimeters) of the tumor in the x -, y -, and z -axes were measured. Distances from the center of the tumor to the chest wall and the adjacent skin were measured on transverse and reformatted sagittal images. In cases where multifocal lesions were present, the transverse, anteroposterior, and craniocaudal distances between the tumor centers in the x -, y -, and z -axes were measured. Differences between measurements in supine and prone MRI were evaluated with the Mann-Whitney U and the Wilcoxon tests. P values of less than 0.05 were considered to indicate a statistically significant difference. The analysis revealed 31 MRIs with 1 and 20 with multifocal lesions. The x -axis dimension of the lesions in prone and supine positions did not significantly differ ( P = 0.198) between the 2 positions. A significant difference in the y - and z -axes dimensions was observed between the prone and supine position ( P = 0.00 for both). The distance from the tumor to the chest wall and the adjacent skin showed significant difference ( P = 0.00 for both). For multifocal lesions, the distance between the lesions showed a significant difference on the y -axis ( P = 0.00). This study showed a significant difference in the tumor size, location, and tumor-to-tumor distances due to change of patient position, from the standard prone MRI to the supine position in the operating room, resulting in distortion, spatial repositioning, and convergence of the lesions. Supine MRI may be considered in providing a less extensive surgery.

Near-Infrared Handheld Probe and Imaging System for Breast Tumor Localization

Near-Infrared Handheld Probe and Imaging System for Breast Tumor Localization

Inverse Estimation of Breast Tumor Size and Location with Numerical Thermal Images of Breast Model Using Machine Learning Models

Early screening of cancer plays a vital role in successful treatment. Normally, the temperature of breast surface changes due to the size and location of the underlying tumor. Temperature patterns can be used to estimate the size and location of tumor by an inverse approach using machine learning algorithms. The present study aims to provide an efficient machine learning model that can be relied on to predict the size and location of the tumor using numerical thermal images. There is no availability of actual thermal images labeled with the size and location of the tumor. Consequently, successive numerical simulations are used to develop the numerical thermal image dataset with a three-dimensional breast model solved with COMSOL Multiphysics. The obtained numerical thermal images are uniquely labeled with the corresponding size and location of the tumor and trained using various machine learning regression models such as linear, support vector, K-nearest neighbor, and decision tree regression models. The results are analyzed using parity plots and mean absolute error. The present study found that the decision tree regression model outperformed the other machine learning models and improved the estimation accuracy by rejecting the numerical thermal images which are having slight variation in surface temperature.

Improved FCOS for Detecting Breast Cancers.

Breast cancer ranks first among cancers affecting women's health. Our goal is to develop a fast, high-precision, and fully automated breast cancer detection algorithm to improve the early detection rate of breast cancer. We compare different object detection algorithms, including anchor-based and anchor-free object detection algorithms for detecting breast lesions. Finally, we find that the fully convolutional onestage object detection (FCOS) showed the best performance in the detection of breast lesions, which is an anchor-free algorithm. 1) Considering that the detection of breast lesions requires the context information of the ultrasound images, we introduce the non-local technique, which models long-range dependency between pixels to the FCOS algorithm, providing the global context information for the detection of the breast lesions. 2) The variety of shapes and sizes of breast lesions makes detection difficult. We propose a new deformable spatial attention (DSA) module and add it to the FCOS algorithm. The detection performance of the original FCOS is that the average precision (AP) for benign lesions is 0.818, and for malignant lesions is 0.888. The FCOS with a non-local module improves the performance of the breast detection; the AP of benign lesions was 0.819, and that of malignant lesions was 0.894. Combining the DSA module with the FCOS improves the performance of breast detection; the AP for benign lesions and malignant lesions is 0.840 and 0.899, respectively. We propose two methods to improve the FCOS algorithm from different perspectives to improve its performance in detecting breast lesions. We find that FCOS combined with DSA is beneficial in improving the localization and classification of breast tumors and can provide auxiliary diagnostic advice for ultrasound physicians, which has a certain clinical application value.

Breast Tumor Detection, Sizing and Localization Using a 24-Element Antenna Array.

This paper proposes a simple and novel design for breast cancer detection, sizing and localization. A miniature-sized antenna of volume 10 × 10 × 1.6 mm3 is designed at an optimized frequency of 7.98 GHz with a positive gain using the fractal theory; it is based on two nested folds connected to each other using repeated lines, discs and circles. It exhibits a reflection coefficient of -39 dB over a simulated bandwidth of 340 MHz; it has a gain of 1.04 dB and an efficiency of around 70%. This prototype is fabricated and its S11 is measured to get an operational frequency of 8.08 GHz with same S11 and bandwidth values. The insertion of slots in the antenna design improved its performance to get a higher [Formula: see text] of 8.21 GHz and an improved gain of 2.43 dB. The SAR value of the antenna is found to be around 1.4 W/Kg at a safe separation distance of 60 mm from the breast. As an array, it proved its ability to detect the presence of the tumor, its size and position. This is simply achieved by plotting the propagating E-field that is induced inside the breast. The main advantage of this system lies in the fact that it does not require any long-time complicated image processing. By observing E-field plots, it is seen that the | Ex | decreased from 8.37 V/m to 1.91 V/m. As for the | Ey |, it increased from 2.72 V/m to 4.12 V/m; both comparisons made between healthy and cancerous tissues, respectively. Simulations are conducted in HFSS; the antenna proved to be a good candidate for breast tumor detection, sizing and localization systems.

Radiotherapy-Related Dose and Irradiated Volume Effects on Breast Cancer Risk Among Hodgkin Lymphoma Survivors.

BackgroundBreast cancer (BC) risk is increased among Hodgkin lymphoma (HL) survivors treated with chest radiotherapy. Case-control studies showed a linear radiation dose-response relationship for estimated dose to the breast tumor location. However, these relative risks cannot be used for absolute risk prediction of BC anywhere in the breasts. Furthermore, the independent and joint effects of radiation dose and irradiated volumes are unclear. Therefore, we examined the effects of mean breast dose and various dose-volume parameters on BC risk in HL patients.MethodsWe conducted a nested case-control study of BC among 5-year HL survivors (173 case patients, 464 matched control patients). Dose-volume histograms were obtained from reconstructed voxel-based 3-dimensional dose distributions. Summary parameters of dose-volume histograms were studied next to mean and median breast dose, Gini index, and the new dose metric mean absolute difference of dose, using categorical and linear excess odds ratio (EOR) models. Interactions between dose-volume parameters and mean dose were also examined.ResultsStatistically significant linear dose-response relationships were observed for mean breast dose (EOR per Gy = 0.19, 95% confidence interval [CI] = 0.05 to 1.06) and median dose (EOR/Gy = 0.06, 95% CI = 0.02 to 0.19), with no statistically significant curvature. All metrics except Gini and mean absolute difference were positively correlated with each other. These metrics all showed similar patterns of dose-response that were no longer statistically significant when adjusting for mean dose. No statistically significant modification of the effect of mean dose was observed.ConclusionMean breast dose predicts subsequent BC risk in long-term HL survivors.

“DIFFERENTIATING BENIGN FROM MALIGNANT BREAST LESIONS- CLINICAL, HISTOPATHOLOGICAL AND IMAGING CORRELATION”

Introduction: Breast cancer is the second most common cancer in Indian women. Breast cancer is a signicant cause of worldwide morbidity and mortality. Mammography and ultrasound are used as the rst line of investigation for the early detection and localization of breast tumors. Mammography has high sensitivity in case of patients with fatty parenchyma however low sensitivity in case of patients with dense breasts, implants, and post-surgical scar, thus breast MRI with higher sensitivity and specicity due to its ability to depict excellent soft-tissue contrast has become increasingly important in the detection of breast cancer. DCE-MRI has improved specicity in characterizing breast lesions by analysis of time-intensity curves. Diffusion-weighted imaging can improve the sensitivity and specicity of MRI in the evaluation of breast lesions by calculating the ADC values a quantitative measure that is a useful tool for tumor detection and differentiating between benign and malignant breast lesions. To evaluate the role of diffusion-weighted MRI andObjective: dynamic contrast-enhanced MRI in differentiating benign from malignant breast lesions and to compare the ndings of diffusion-weighted MRI and dynamic enhanced MRI with histopathological or FNAC ndings. A totalMaterial And Methods: of 30 patients with palpable breast lumps with either positive or negative ndings on mammography and ultrasound were included in this study. All patients included in this study rst underwent lm-screen mammography. Ultrasonography was done with convex and linear probes. This was followed by MRI. T1W axial, T2W axial, SPAIR/ Fat Saturated T2 weighted axial images were acquired in appropriate imaging planes. Diffusion-weighted images were obtained using b values of 0 and 1000 and ADC values were calculated. Dynamic contrast-enhanced MR was performed using fat-suppressed 3D T1 weighted images after injection of gadolinium and time-intensity curves were generated. A single precontrast scan was followed by 4 post- contrast scans which were obtained for a total duration of 4 min 24 seconds. Findings of the MRI (Diffusion-weighted and dynamic contrast enhancement) were analyzed and correlated with histopathological and FNAC ndings to evaluate their use as a diagnostic modality. A total of 30 female patients presenting with palpable breast lumps were included in theResults: study. Out of 30 patients, 20 cases were malignant (66.67%) and 10 cases were benign (33.3%). Fibroadenoma accounted for a majority of benign lesions (4 out of 10 benign lesions) while IDC accounted for the majority of malignant lesions (15 out of 20 malignant lesions). Dynamic CE-MR is a reliable tool for differentiating between benign and malignant lesions based on kinetic curves. 7 out of 10 benign lesions showed a type I curve while the rest showed a type II curve while the majority (15/20) of malignant lesions showed a type III curve. 8 out of 10 benign breast lesions did not show restricted diffusion on DWI while all malignant lesions showed restricted diffusion on DWI. In our study, the mean ADC value for benign lesions was 1.59 x 10 mm/s while the mean ADC value for malignant lesions was 0.88 x 10 mm/s. Using the ROC curve, the cut-off value of ADC was calculated to be 1.19 x 10 mm/s which gives sensitivity and specicity of 95% and 90% respectively. The individual sensitivity for DCE-MRI and DWI was calculated to be 95% and 95% while the individual specicity for DCE-MRI and DWI was calculated to be 70% and 90% respectively. After a combined analysis of DCE-MRI and DWI using a positive result from any of the two techniques as malignancy, the sensitivity and specicity were 95% and 80% respectively.

Impact of distress screening and referral on health care utilization and cost among breast cancer patients: a retrospective cohort study ∗

Abstract Background: Addressing cancer patient distress and unmet needs may reduce health care costs and enhance care utilization. This study examined the impact of a distress screening program CancerSupportSource™ (CSS) on health care utilization and costs for breast cancer patients. Methods: In a retrospective cohort study with 2 years’ follow-up, breast cancer patients receiving care at a community cancer center in Orlando, FL, between 2016 and 2019 were categorized according to exposure status: screened using CSS and acted by using supportive care services (SA); screened only (SO); and not screened (NS). Patients were matched on breast tumor location and age; screened patients were additionally matched on referral need. Outcomes abstracted from medical records included utilization and cost of emergency department (ED) services, hospital inpatient admissions, and outpatient services; and utilization of integrative medicine department, patient/family counseling, and allied health services. Results: SA patients (n = 36), compared to NS (n = 37), had significantly lower rates of ED visits in negative binomial regression analysis (incidence rate ratio [IRR] 0.43; 95% confidence interval [CI] 0.20–0.93; P = .031). There were significantly higher rates of integrative medicine department visits in SA (IRR 4.20; 95% CI 1.63–10.9; P = .003) and SO (IRR 3.71; 95% CI 1.49–9.24; P = .005) groups compared to NS, and higher rates of patient/family counseling visits in SA (IRR 6.21; 95% CI 1.52–25.3; P = .011). There were no significant differences in 2-year health care costs between groups, controlling for age and race/ethnicity. Conclusions: These findings highlight the potential value of distress screening and referral for health care utilization, shifting use of higher cost services to lower cost nonemergent and preventive care in cancer, and can inform future prospective research on cost outcomes.