Surface Engineering of Magnetic Iron Oxide Nanoparticles for Breast Cancer Diagnostics and Drug Delivery.

Data published in 2020 by the International Agency for Research on Cancer (IARC) of the World Health Organization show that breast cancer (BC) has become the most common cancer globally, affecting more than 2 million women each year. The complex tumor microenvironment, drug resistance, metastasis, and poor prognosis constitute the primary challenges in the current diagnosis and treatment of BC. Magnetic iron oxide nanoparticles (MIONPs) have emerged as a promising nanoplatform for diagnostic tumor imaging as well as therapeutic drug-targeted delivery due to their unique physicochemical properties. The extensive surface engineering has given rise to multifunctionalized MIONPs. In this review, the latest advancements in surface modification strategies of MIONPs over the past five years are summarized and categorized as constrast agents and drug delivery platforms. Additionally, the remaining challenges and future prospects of MIONPs-based targeted delivery are discussed.

Prediction of disease-free survival using strain elastography and diffuse optical tomography in patients with T1 breast cancer: a 10-year follow-up study.

BACKGROUND: Early-stage breast cancer (BC) presents a certain risk of recurrence, leading to variable prognoses and complicating individualized management. Yet, preoperative noninvasive tools for accurate prediction of disease-free survival (DFS) are lacking. This study assessed the potential of strain elastography (SE) and diffuse optical tomography (DOT) for non-invasive preoperative prediction of recurrence in T1 BC and developed a prediction model for estimating the probability of DFS. METHODS: A total of 565 eligible patients with T1 invasive BC were enrolled prospectively and followed to investigate the recurrence. The associations between imaging features and DFS were evaluated and a best-prediction model for DFS was developed and validated. RESULTS: During the median follow-up period of 10.8 years, 77 patients (13.6%) developed recurrences. The fully adjusted Cox proportional hazards model showed a significant trend between an increasing strain ratio (SR) (P < 0.001 for trend) and the total hemoglobin concentration (TTHC) (P = 0.001 for trend) and DFS. In the subgroup analysis, an intensified association between SR and DFS was observed among women who were progesterone receptor (PR)-positive, lower Ki-67 expression, HER2 negative, and without adjuvant chemotherapy and without Herceptin treatment (all P < 0.05 for interaction). Significant interactions between TTHC status and the lymphovascular invasion, estrogen receptor (ER) status, PR status, HER2 status, and Herceptin treatment were found for DFS(P < 0.05).The imaging-clinical combined model (TTHC + SR + clinicopathological variables) proved to be the best prediction model (AUC = 0.829, 95% CI = 0.786-0.872) and was identified as a potential risk stratification tool to discriminate the risk probability of recurrence. CONCLUSION: The combined imaging-clinical model we developed outperformed traditional clinical prognostic indicators, providing a non-invasive, reliable tool for preoperative DFS risk stratification and personalized therapeutic strategies in T1 BC. These findings underscore the importance of integrating advanced imaging techniques into clinical practice and offer support for future research to validate and expand on these predictive methodologies.

Utilizing multiclassifier radiomics analysis of ultrasound to predict high axillary lymph node tumour burden in node-positive breast cancer patients: a multicentre study.

BACKGROUND: The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis. OBJECTIVE: This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer. METHODS: A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model. RESULTS: During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model. CONCLUSIONS: The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.

Initial interpretation scores of screening mammograms and cancer detection in BreastScreen Norway.

PURPOSE: To explore the association between radiologists' interpretation scores, early performance measures and cumulative reading volume in mammographic screening. METHOD: We analyzed 1,689,731 screening examinations (3,379,462 breasts) from BreastScreen Norway 2012-2020, all breasts scored 1-5 by two independent radiologists. Score 1 was considered negative/benign and score ≥2 positive in this scoring system. We performed descriptive analyses of recall, screen-detected cancer, positive predictive value (PPV) 1, mammographic features and histopathological characteristics by breast-based interpretation scores, and cumulative reading volume by examination-based interpretation scores. RESULTS: Counting breasts and not women, 3.9 % (132,570/3,379,462) had a score of ≥2 by one or both radiologists. Of these, 84.8 % (112,440/132,570) were given a maximum score 2. Total recall rate was 1.6 % (53,735/3,379,462), 69.3 % (37,220/53,735) given maximum score 2. Among the 0.3 % (9733/3,379,462) diagnosed with screen-detected cancer, 34.6 % (3369/9733) had maximum score 3. The percentages of recall, screen-detected cancer and PPV-1 increased by increasing the sum of scores assigned by two radiologists (p < 0.001 for trend). Higher proportions of masses were observed among recalls and screen-detected cancers with low scores, and higher proportions of spiculated masses were observed for high scores (p < 0.001). Proportions of invasive carcinoma, histological grade 3 and lymph node positive tumors were higher for high versus low scores (p < 0.001). The proportion of examinations scored 1 increased by cumulative reading volume. CONCLUSIONS: We observed higher rates of recall and screen-detected cancer and less favorable histopathological tumor characteristics for high versus low interpretation scores. However, a considerable number of recalls and screen-detected cancers had low interpretation scores.

Role of Combining Grayscale Findings With Superb Microvascular Imaging and Shear Wave Elastography in Standardization and Management of NON-MASS Breast Lesions.

ABSTRACT: The non-mass breast lesions on ultrasound (US) are a group of challenging pathology. We aimed to standardize these grayscale findings and investigate the effectiveness of superb microvascular imaging (SMI) and shear wave elastography (SWE). A total of 195 lesions were evaluated by B-mode US, SWE, and SMI in the same session. A "NON-MASS model" was built on grayscale US to group the lesions only as areas and those with associated features: microcalcifications, architectural distortion, ductal changes, and microcysts. The mean stiffness parameters Emean, Eratio, and mean vascular index (VI) were recorded following consecutive measurements. Besides, the microvascularity was graded based on Adler's classification (grades 0 to 3). Lesions were divided into 3 groups: benign, category B3, and malignant. One hundred twelve (57.4%) lesions were benign, 23 (11.8%) were B3, and 60 were (30.8%) in the malignant category. Thirty-eight (19.5%) lesions were observed only as an area, whereas associated features were present in 157 lesions (80.5%). Distortion was the only associated feature predicting malignancy among the grayscale findings (P < 0.001). There was a significant difference between malignant and nonmalignant (benign and B3) groups in terms of Adler's grade, Emean, Eratio, and VI values (P < 0.001). Sensitivity, specificity, and accuracy increased when advanced imaging parameters were added to grayscale findings (P < 0.001). In the presence of microcalcifications, architectural distortion, high elasticity, and hypervascularity in the "NON-MASS" imaging model, the suspicion of malignancy increases. The non-mass findings and advanced imaging techniques have the potential to find greater coverage in the following versions of BI-RADS atlas.

Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

BACKGROUND: Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations. OBJECTIVE: This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images. METHODS: The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). RESULTS: The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%). CONCLUSION: Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.

Estrogen Receptor-targeted PET Imaging for Breast Cancer.

Two complementary patient cases are presented to highlight the importance of estrogen receptor (ER)-targeting imaging in treatment planning and selection for endocrine therapy in breast cancer patients. This article will discuss the radiopharmaceuticals and biology, imaging interpretation, and current clinical applications of ER-targeting imaging using fluorine 18 fluoroestradiol PET.

[BIRADS 0 patient reclassification in a first-level of care unit]. / Reclasificación de pacientes BIRADS 0 en una unidad de primer nivel de atención.

Background: In Mexico and the world, breast cancer is the cancer type with the highest incidence and mortality for women. Its incidence has increased due to a higher life expectancy and a higher exposure to risk factors. Screening is done by mammography using the BIRADS (Breast Imaging Reporting and Data System) system, the standard for mammography screening report which classifies lesions assigning recommendations for patient follow-up. The system goes from 0 (not conclusive) to 6 (demonstrated malignancy), being of interest for this study the BIRADS 0 category. Objective: To describe patients classified as BIRADS 0 by mammography and their reclassification in a first-level hospital during 2021. Material and methods: Retrospective, descriptive, cross-sectional, observational study. Women over 40 years with a BIRADS 0 result were studied. The following databases were used: Institutional Cancer Registry, Family Medicine Information System, Electronic Clinical File, and the mammography and patient list from preventive medicine. Results: Reclassification by ultrasound (US) was achieved in 100% of patients, in all of the BIRADS US categories. In 3.8% of BIRADS 0 patients, ductal adenocarcinoma was found and confirmed by histological testing. Conclusion: All of the reassessed lesions with US were adequately reclassified.

Introducción: en México y el mundo, el cáncer de mama causa la mayor mortalidad por cáncer en mujeres. Su incidencia ha incrementado por una mayor esperanza de vida y exposición a factores de riesgo. El tamizaje de esta enfermedad se hace mediante mastografía, y para la estratificación de las lesiones se utiliza el sistema BIRADS (Breast Imaging Reporting and Data System), que estandariza el informe, categoriza las lesiones según el grado de sospecha y asigna recomendaciones a seguir. Dicho sistema va desde 0 (no concluyente) hasta 6 (lesión con malignidad demostrada) y es de interés para este estudio la categoría 0. Objetivo: describir la reclasificación de pacientes con reporte BIRADS 0 por mastografía durante 2021 en una unidad de primer nivel de atención. Material y métodos: estudio retrospectivo, descriptivo, transversal, observacional. Se estudiaron mujeres mayores de 40 años con resultado BIRADS 0. Se utilizaron las siguientes bases de datos: Registro Institucional de Cáncer, Sistema de Información de Medicina Familiar, Expediente Clínico Electrónico y lista nominal de mastografías y censo de pacientes sospechosos de medicina preventiva. Resultados: la reclasificación con ultrasonido (US) se logró en el 100% de pacientes, en todas las categorías de BIRADS US. En el 3.8% se confirmó carcinoma ductal por histología en las pacientes inicialmente categorizadas como BIRADS 0. Conclusiones: la totalidad de lesiones reevaluadas con US fueron reclasificadas satisfactoriamente.

Incidence-Based Breast Cancer Mortality Trends in Estonia Before and After the Introduction of Organized Mammography Screening: A Register-Based Study.

BACKGROUND: Despite the relatively low breast cancer incidence in Estonia, mortality remains high, and participation in mammography screening is below the recommended 70%. The objective of this register-based study was to evaluate incidence-based (IB) breast cancer mortality before and after the introduction of organized mammography screening in 2004. METHODS: Breast cancer deaths individually linked to breast cancer diagnosis were obtained from the Estonian Cancer Registry and used for calculating IB mortality. We compared age-specific IB mortality rates across 5-year birth cohorts and 5-year periods. Poisson regression was used to compare IB mortality for one age group invited to screening (50-63) and three age groups not invited to screening (30-49, 65-69, and 70+) during two periods before and after screening initiation (1993-2003 and 2004-2014). Joinpoint regression was used for age-standardized incidence and IB mortality trends. RESULTS: Age-standardized IB mortality has been decreasing since 1997. Age-specific IB mortality for birth cohorts never exposed to screening showed a continuous increase with age, while in cohorts exposed to organized screening the mortality curve flattened or declined after the age of first invitation. Significant decreases in mortality from 1993-2003 to 2004-2014 were seen in the 30-49 (age-adjusted rate ratio 0.51, 95% CI 90.42-0.63) and 50-63 (0.65, 95% CI 0.56-0.74) age groups, while no decline was seen in the 65-69 and 70+ age groups. CONCLUSIONS: The age specific IB mortality curves in birth cohorts exposed to screening and the significant mortality decline in the target age group after the initiation of the organized program suggest a beneficial effect of screening. Improved treatment without screening has not reduced mortality in older age groups. Our results support raising the upper screening age limit to 74 years.

Automated system utilizing non-invasive technique mammograms for breast cancer detection.

In order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient's therapy and recovery is crucial. This study's objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic). To ascertain if the tissue is malignant or benign, the examination's findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95.32%, while the RF satisfied a far higher 98.83 percent.

Ultrasound-guided photoacoustic (US-PA) tomography of the breast: Biochemical differentiation using intrinsic tissue markers-lipids, collagen and hemoglobin with histopathologic correlation.

In this pilot study, we investigated the utility of handheld ultrasound-guided photoacoustic (US-PA) imaging probe for analyzing ex-vivo breast specimens obtained from female patients who underwent breast-conserving surgery (BCS). We aimed to assess the potential of US-PA in detecting biochemical markers such as collagen, lipids, and hemoglobin, and compare these findings with routine imaging modalities (mammography, ultrasound) and histopathology results, particularly across various breast densities. Twelve ex-vivo breast specimens were obtained from female patients with a mean age of 59.7 ± 9.5 years who underwent BCS. The tissues were illuminated using handheld US-PA probe between 700 and 1100 nm across all margins and analyzed for collagen, lipids, and hemoglobin distribution. The obtained results were compared with routine imaging and histopathological assessments. Our findings revealed that lipid intensity and distribution decreased with increasing breast density, while collagen exhibited an opposite trend. These observations were consistent with routine imaging and histopathological analyses. Moreover, collagen intensity significantly differed (P < 0.001) between cancerous and normal breast tissue, indicating its potential as an additional biomarker for risk stratification across various breast conditions. The study results suggest that a combined assessment of PA biochemical information, such as collagen and lipid content, superimposed on grey-scale ultrasound findings could aid in distinguishing between normal and malignant breast conditions, as well as assist in BCS margin assessment. This underscores the potential of US-PA imaging as a valuable tool for enhancing breast cancer diagnosis and management, offering complementary information to existing imaging modalities and histopathology.

Evaluation of axillary lymph nodes in breast cancer patients with clinically negative axilla using contrast enhanced ultrasonography.

Contrast enhanced ultrasonography enables dynamic evaluation of the microvasculature down to the capillaries when using high resolution ultrasound probes. It's application in the evaluation of axillary lymph nodes in breast cancer patients with clinically negative axilla has been studied in 42 patients. The results of pre operative CEUS evaluation was correlated with histopathology status of axillary nodes after the harvesting of nodes during modified radical mastectomy or sentinel node biopsy. Heterogeneous enhancement with micro bubbles of the axillary nodes was found to be the most distinguishing criteria for malignant nodes.

Evaluation of the intramammary distribution of breast lesions detected by MRI but not conventional second-look B-mode ultrasound using an MRI/ultrasound fusion technique.

The objective of this study was to evaluate the intramammary distribution of MRI-detected mass and focus lesions that were difficult to identify with conventional B-mode ultrasound (US) alone. Consecutive patients with lesions detected with MRI but not second-look conventional B-mode US were enrolled between May 2015 and June 2023. Following an additional supine MRI examination, we performed third-look US using real-time virtual sonography (RVS), an MRI/US image fusion technique. We divided the distribution of MRI-detected mammary gland lesions as follows: center of the mammary gland versus other (superficial fascia, deep fascia, and atrophic mammary gland). We were able to detect 27 (84%) of 32 MRI-detected lesions using third-look US with RVS. Of these 27 lesions, 5 (19%) were in the center of the mammary gland and 22 (81%) were located in other areas. We were able to biopsy all 27 lesions; 8 (30%) were malignant and 19 (70%) were benign. Histopathologically, three malignant lesions were invasive ductal carcinoma (IDC; luminal A), one was IDC (luminal B), and four were ductal carcinoma in situ (low-grade). Malignant lesions were found in all areas. During this study period, 132 MRI-detected lesions were identified and 43 (33%) were located in the center of the mammary gland and 87 (64%) were in other areas. Also, we were able to detect 105 of 137 MRI-detected lesions by second-look conventional-B mode US and 38 (36%) were located in the center of the mammary gland and 67 (64%) were in other areas. In this study, 81% of the lesions identified using third-look US with RVS and 64% lesions detected by second-look conventional-B mode US were located outside the center of the mammary gland. We consider that adequate attention should be paid to the whole mammary gland when we perform third-look US using MRI/US fusion technique.

The Chiraiya project: a retrospective analysis of breast cancer detection gaps addressed via mobile mammography in Jammu Province, India.

BACKGROUND: Breast cancer remains a pervasive threat to women worldwide, with increasing incidence rates necessitating effective screening strategies. Timely detection with mammography has emerged as the primary tool for mass screening. This retrospective study, which is part of the Chiraiya Project, aimed to evaluate breast lesion patients identified during opportunistic mammography screening camps in Jammu Province, India. METHODS: A total of 1505 women aged 40 years and older were screened using a mobile mammographic unit over a five-year period, excluding 2020 and 2021 due to the COVID-19 pandemic. The inclusion criterion was women in the specified age group, while the exclusion criterion was women with open breast wounds, history of breast cancer or a history of breast surgery. The screening process involved comprehensive data collection using a detailed Proforma, followed by mammographic assessments conducted within strategically stationed mobile units. Radiological interpretations utilizing the BI-RADS system were performed, accompanied by meticulous documentation of patient demographics, habits, literacy, medical history, and breastfeeding practices. Participants were recruited through collaborations with NGOs, army camps, village panchayats, and urban cooperatives. Screening camps were scheduled periodically, with each camp accommodating 90 patients or fewer. RESULTS: Among the 1505 patients, most were aged 45-50 years. The number of screenings increased yearly, peaking at 441 in 2022. The BI-RADS II was the most common finding (48.77%), indicating the presence of benign lesions, while the BI-RADS 0 (32.96%) required further evaluation. Higher-risk categories (BI-RADS III, IV, V) were less common, with BI-RADS V being the rarest. Follow-up adherence was highest in the BI-RADS III, IV, and V categories, with BI-RADS V achieving 100% follow-up. However, only 320 of 496 BI-RADS 0 patients were followed up, indicating a gap in continuity of care. The overall follow-up rate was 66.89%. Compared to urban areas, rural areas demonstrated greater screening uptake but lower follow-up rates, highlighting the need for tailored interventions to improve follow-up care access, especially in rural contexts. CONCLUSION: This study underscores the efficacy of a mobile mammographic unit in reaching marginalized populations. Adherence to screening protocols has emerged as a linchpin for early detection, improved prognosis, and holistic public health enhancement. Addressing misconceptions surrounding mammographic screenings, especially in rural settings, is crucial. These findings call for intensified efforts in advocacy and education to promote the benefits of breast cancer screening initiatives. Future interventions should prioritize improving access to follow-up care and addressing screening to enhance breast cancer management in Jammu Province.

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

INTRODUCTION: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Surgical treatment of breast precancers - our experience.

INTRODUCTION: Thanks to mammographic screening and the improvement of breast cancer diagnostics, the detection of precancers is also increasing. They are defined as morphological changes of the mammary gland which are more likely to cause cancer. The evaluated precancers are atypical ductal hyperplasia (ADH), lobular carcinoma in situ (LCIS) and radial scar. METHODOLOGY: In the period 1. 1. 2018-31. 12. 2022, we performed 1,302 planned operations for breast disease at the Surgical Clinic of Teaching Hospital Plzen, of which 30 (2%) were precancer operations. ADH was confirmed 11×, LCIS 8×, and a radical scar 11×. The average age of the patients in all three groups was 56 years (27-85). Precancer was diagnosed 8× only by sonography, 3× by mammography and 19× by a combination of both methods. Subsequently, a puncture biopsy was always completed. We performed 28 tumor excisions with intraoperative biopsy and 2 mastectomies. RESULTS: In the case of ADH from puncture biopsy, ADH was confirmed intraoperatively 8×, DCIS was diagnosed 2×, and mucinous carcinoma 1×. In LCIS, no tumor was found by intraoperative biopsy 4×, LCIS was confirmed 1×, lobular invasive carcinoma was diagnosed 1×, mastectomy was performed 2× without intraoperative biopsy. In the radial scar, ADH was diagnosed 3×, sclerosing adenosis 6×, DCIS 1×, invasive carcinoma 1×. After the final histological processing of the samples, there was an increase in diagnosed carcinomas. In ADH, DCIS was confirmed 3×, DIC 2×, and mucinous carcinoma 1×. In LCIS, LIC was diagnosed 3×. In the radial scar, DCIS was confirmed 1×, and invasive carcinoma remain 1×. Thus, carcinoma was diagnosed in 11 patients (37%) thanks to the surgical solution. No patient underwent axillary node surgery. All 11 patients subsequently underwent oncological treatment, always a combination of radiotherapy and hormone therapy. All patients are alive, 10 patients are in complete remission of the disease, one with DCIS experienced a local recurrence after 4 years. CONCLUSION: Surgical treatment of precancers of the breast makes sense, DCIS or even invasive cancer is often hidden in addition to precancer. Thanks to the surgical solution, the cancer was detected in time.

Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients.

PURPOSE: This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT). MATERIALS AND METHODS: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy. RESULTS: In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD. CONCLUSION: Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

Croconaine-based NIR-II fluorescence imaging-guided tumor photothermal therapy induces long-term antitumor immune memory.

Photothermal therapy (PTT) for cancers guided by optical imaging has recently shown great potential for precise diagnosis and efficient therapy. The second near-infrared window (NIR-II, 1000-1700 nm) fluorescence imaging (FLI) is highly desirable owing to its good spatial and temporal resolution, deep tissue penetration, and negligible tissue toxicity. Organic small molecules are attractive as imaging and treatment agents in biomedical research because of their low toxicity, fast clearance rate, diverse structures, ease of modification, and excellent biocompatibility. Various organic small molecules have been investigated for biomedical applications. However, there are few reports on the use of croconaine dyes (CRs), especially NIR-II emission CRs. To our knowledge, there have been no prior reports of NIR-II emissive small organic photothermal agents (SOPTAs) based on CRs. Herein, we report a croconaine dye (CR-TPE-T)-based nanoparticle (CR NP) with absorption and fluorescence emission in the NIR-I and NIR-II windows, respectively. The CR NPs exhibited intense NIR absorption, outstanding photothermal properties, and good biological compatibility. In vivo studies showed that CR NPs not only achieved real-time, noninvasive NIR-II FLI of tumors, but also induced significant tumor ablation with laser irradiation guided by imaging, without apparent side effects, and promoted the formation of antitumor immune memory in a colorectal cancer model. In addition, the CR NPs displayed efficient inhibition of breast tumor growth, improved longevity of mice and triggered efficient systemic immune responses, which further inhibited tumor metastasis to the lungs. Our study demonstrates the great potential of CRs as therapeutic agents in the NIR-II region for cancer diagnosis.

Theranostics Using MCM-41-Based Mesoporous Silica Nanoparticles: Integrating Magnetic Resonance Imaging and Novel Chemotherapy for Breast Cancer Treatment.

Advanced breast cancer remains a significant oncological challenge, requiring new approaches to improve clinical outcomes. This study investigated an innovative theranostic agent using the MCM-41-NH2-DTPA-Gd3⁺-MIH nanomaterial, which combined MRI imaging for detection and a novel chemotherapy agent (MIH 2.4Bl) for treatment. The nanomaterial was based on the mesoporous silica type, MCM-41, and was optimized for drug delivery via functionalization with amine groups and conjugation with DTPA and complexation with Gd3+. MRI sensitivity was enhanced by using gadolinium-based contrast agents, which are crucial in identifying early neoplastic lesions. MIH 2.4Bl, with its unique mesoionic structure, allows effective interactions with biomolecules that facilitate its intracellular antitumoral activity. Physicochemical characterization confirmed the nanomaterial synthesis and effective drug incorporation, with 15% of MIH 2.4Bl being adsorbed. Drug release assays indicated that approximately 50% was released within 8 h. MRI phantom studies demonstrated the superior imaging capability of the nanomaterial, with a relaxivity significantly higher than that of the commercial agent Magnevist. In vitro cellular cytotoxicity assays, the effectiveness of the nanomaterial in killing MDA-MB-231 breast cancer cells was demonstrated at an EC50 concentration of 12.6 mg/mL compared to an EC50 concentration of 68.9 mg/mL in normal human mammary epithelial cells (HMECs). In vivo, MRI evaluation in a 4T1 syngeneic mouse model confirmed its efficacy as a contrast agent. This study highlighted the theranostic capabilities of MCM-41-NH2-DTPA-Gd3⁺-MIH and its potential to enhance breast cancer management.

Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.

To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.