Breast Cancer Treatment Planning Research Articles

The Prevalence of BRCA1 and BRCA2 Mutations in Breast Cancer Patients

Aim: Genetic mutations in BRCA1 and BRCA2 are crucial for breast cancer risk assessment and treatment planning. Methods: This prospective observational study at Jinnah Postgraduate Medical Centre, Pakistan, involved female breast cancer patients aged 18 or older. The sample size calculation was based on a 3.4% prevalence rate of BRCA mutations, aiming for a 95% confidence level. Data on demographic, clinical characteristics, and genetic testing for BRCA mutations were collected and analyzed using SPSS version 23. Results: Among the study participants, 10% exhibited BRCA1 or BRCA2 mutations. The majority were diagnosed at stage 3 tumor development, with invasive ductal carcinoma being the predominant histological type. No significant familial predisposition to breast cancer was noted among the majority. Educational status and ethnicity showed varying associations with BRCA mutation presence. Conclusion: The study highlights a modest incidence of BRCA mutations among Pakistani breast cancer patients, underscoring the importance of genetic testing for risk assessment and targeted treatment. The findings support the need for comprehensive genetic screening programs in Pakistan, considering the diverse demographic and clinical characteristics of the population. Keywords: BRCA mutations, breast cancer, genetic testing, Pakistan, risk assessment, targeted treatment.

Abstract PO5-14-03: Analytical Validation of the StemPrintER Assay in Patients with Breast Cancer

Abstract Discussion: Breast cancer is the most common malignancy diagnosed amongst women, with more the two million new cases identified globally each year. Thus, considerable resources have been devoted to a better understanding of the disease and how to optimize outcomes for patients. Genetics and genomics have become part of routine care with the latter used to inform treatment planning for patients with ER+ breast cancer (ER+ BC). A multitude of assays may be used to determine the need for adjuvant chemotherapy but none of the current tests on the market are guideline-recommended for making decisions related to neoadjuvant chemotherapy, surgery, or radiation. The 24-gene StemPrintER assay was developed to measure the “stemness” of tumors, or how much a tumor behaves like stem cells, which could have important and novel implications for breast cancer treatment planning. The assay has been validated for predicting prognosis using formalin-fixed paraffin-embedded (FFPE) tissue blocks from several clinical cohorts, including TransATAC. The goal of current and future research will be to determine whether the assay can determine the ideal surgical intervention for ER+BC patients. Early in silico work demonstrated a trend toward this predictive capacity for the assay and suggests the assay may be able to identify the benefit of mastectomy versus quadrantectomy. Here, we report on the ability to set up and run the assay in our lab using synthetic oligonucleotides and archival tissue blocks. Methods: Synthetic oligonucleotides and archival FFPE breast cancer tissue were used to set up and analytically validate the assay’s performance in two phases. The initial phase was intended to confirm the individual assay design performance by assessing linearity and precision and the ability to amplify RNA from FFPE, which confirmed compatibility with standard extraction methods. The second phase was intended to determine whether these methods could be implemented to reliably extract and amplify RNA from FFPE >10 years old (range: 10-14 years) in order to generate a StemPrintER score. The assay was run according to standard operating procedures for StemPrintER. Analyses were conducted Python 3.9.12. Results: Two assay runs with duplicate samples for each run using a 7-point dilution series demonstrated linearity (coefficient of correlation, r2) results ranging from 0.978 to 0.999 for each of the 24 genes in the assay. Amplification efficiency was between 100-101% for each gene. Using 14 FFPE samples from patients with triple-negative breast cancer, we demonstrated a consistent extraction yield of RNA from tumor blocks which produced amplification of each of the targets. Analysis of intra- and inter-run precision resulted in coefficients of variation (CV) ranging from 0.2-1.07% and inter-run CV’s ranging from 0.66%-5.56%. Utilizing FFPE >10 years old from ER+BC patients demonstrated that the lab could extract RNA from 100% of samples and amplify the material to obtain binary StemPrintER results. Conclusion: These data demonstrate that StemPrintER can be run reliably on FFPE tissue up to 14 years old. In addition, our laboratory has demonstrated that assay results are highly linear and reproducible. Additional studies are planned to build on preliminary surgical prediction data and determine whether the assay can meet the unmet need of identifying the ideal surgical intervention in patients with early-stage ER+/HER2- breast cancer. Citation Format: Catie Cronister, Brock Schweitzer, Hannah Gilmore, Tyler Nielsen. Analytical Validation of the StemPrintER Assay in Patients with Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-14-03.

Assessing Trends in Hospitalizations for Breast Cancer among Women in Korea: A Utilization of the Korea National Hospital Discharge In-depth Injury Survey (2006–2020)

ObjectiveBreast cancer poses a significant health threat globally and particularly in Korea, where mortality rates have risen notably. In this study, we analyzed the characteristics of breast cancer patients discharged in Korea over the past 15 years and explored the association between comorbidities and treatment outcomes to propose effective strategies for managing cancer patients. Understanding these dynamics is vital for informing tailored management strategies and optimizing healthcare system sustainability.MethodsThis study utilized cross-sectional data from the Korea National Hospital Discharge In-depth Injury Survey from 2006 to 2020. Each year, among patients discharged from hospital with 100 beds or more, those identified with breast cancer patients were based on their primary diagnosis code (C50) according to the ICD-10, as recorded in their medical records.ResultsBetween 2006 and 2020, an estimated 499,281 breast cancer patients were discharged, with an average annual percent change (AAPC) of 5.2% (95% CI 4.2–6.2, p <.05). A notable increase in AAPC was particularly evident among those aged 60 years and old. Across all age groups, there was a consistent increasing trend in the risk of mortality as the CCI score increased (p <.05). The risk of comorbidity was more pronounced in younger age groups compared to older age groups.ConclusionsThe increasing life expectancy is expected to lead to a continued rise in the number of elderly breast cancer patients. Countermeasures are needed to address this trend through appropriate diagnosis and treatment planning. Particularly, considering comorbidities in breast cancer treatment plans is necessary to promote positive treatment outcomes, especially in younger breast cancer patients.

Tomotherapy in synchronous and metachronous bilateral breast cancer: Clinical experience.

The objective of this research is to present our firsthand experience and provide up-to-date data for the further study of cases involving simultaneous breast irradiation using helical Tomotherapy, ©Accuray Inc. The radical treatment options for bilateral breast cancer are surgery, chemotherapy, and radiation therapy. Being that radiotherapy for bilateral breast cancer is challenging due to limitations in the geometry of modern radiotherapy equipment, helical Tomotherapy was chosen as an appropriate technique of irradiation. The retrospective review focused on the records of patients who underwent bilateral irradiation of the breast or chest wall and regional lymph nodes using helical Tomotherapy. Only four patients with bilateral breast cancer completed a radiation therapy course in our center from 2018 to 2023. Two patients underwent radical mastectomy with lymph node dissection on both sides before irradiation. For the other two patients, radical mastectomy was done after neoadjuvant chemotherapy. Acute radiation toxicity scoring was based on Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Only mild adverse effects, such as general weakness and slight skin irritation below Grade 3, were observed, with no instances of skin swelling, dryness, or pigmentation noted. Evaluation of late complications revealed tissue fibrosis in the area of the internal mammary nodes and respiratory failure with various severity. Complications and deterioration in the cardiovascular system were not observed during the follow-up period, which varied from 3 to 48months. Our results show the efficacy of using helical Tomotherapy considering positive outcomes, being that three out of four patients are in remission with low acute toxicity and late complications. There are a small number of articles describing bilateral breast cancer treatment with helical Tomotherapy. On this occasion, our data could contribute to the studies of tolerant doses for organs at risk and improve the parameters of treatment plans for bilateral breast cancer. Since the small sample of patients with bilateral breast cancer limits the study, a larger cohort of patients is essential to obtain statistically reliable results.

Radiotherapy Planning in Synchronous Bilateral Breast Cancer Irradiation: A Dosimetric Study

Background: The large radiation field and surrounding normal structures make treatment planning and dose delivery of synchronous bilateral breast cancer (SBBC) much more difficult than it is for unilateral breast cancer. Intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) can produce acceptable normal tissue sparing while improving target dose coverage compared to 3D-CRT. With this context in mind, we carried out a study to examine the dosimetric variations of IMRT and VMAT techniques using a single isocenter for planning cases of SBBC. Patients and methods: Five patients were considered for this study. All the patients had undergone bilateral mastectomy. Dose prescribed was 50 Gy in 25 fractions. VMAT and IMRT plans were created for each patient. All the plans made were mono-isocentric. Dosimetric data of the target and organs at risk (OAR) were compared. Results: There is a trend towards better PTV coverage using VMAT compared to IMRT (V95 IMRT=94.8720, V95 VMAT=95.8740; P=0.316). VMAT plans significantly improved the dose homogeneity as V105% decreased from 2.886% to 0.312% using VMAT plans (P=0.001). IMRT plans showed trend towards higher lung Dmean and V20Gy than VMAT plans. The irradiation dose to the heart was significantly higher in the IMRT plan than VMAT plan, resulting in Dmean of 15.836 Gy vs 13.580 Gy (P=0.026), V25Gy of 16.498% vs 13.832% (P=0.046) and V30 Gy of 9.774% vs 7.022% (P= 0.043). Conclusion: The single isocentric VMAT technique can be performed conveniently in SBBC. It improves target volume coverage with optimal normal tissue sparing. It is a feasible approach while planning synchronous bilateral breast irradiation.&#x0D;

Evaluation of complexity and deliverability of IMRT treatment plans for breast cancer

This study aimed to predict the outcome of patient specific quality assurance (PSQA) in IMRT for breast cancer using complexity metrics, such as MU factor, MAD, CAS, MCS. Several breast cancer plans were considered, including LBCS, RBCS, LBCM, RBCM, left breast, right breast and the whole breast for both Edge and TrueBeam LINACS. Dose verification was completed by Portal Dosimetry (PD). The receiver operating characteristic (ROC) curve was employed to determine whether the treatment plans pass or failed. The area under the curve (AUC) was used to assess the classification performance. The correlation of PSQA and complexity metrics was examined using Spearman’s rank correlation coefficient (Rs). For LINACS, the most suitable complexity metric was found to be the MU factor (Edge Rs = − 0.608, p < 0.01; TrueBeam Rs = − 0.739, p < 0.01). Regarding the specific breast cancer categories, the optimal complexity metrics were as follows: MAD (AUC = 0.917) for LBCS, MCS (AUC = 0.681) for RBCS, MU factor (AUC = 0.854) for LBCM and MAD (AUC = 0.731) for RBCM. On the Edge LINAC, the preferable method for breast cancers was MCS (left breast, AUC = 0.938; right breast, AUC = 0.813), while on the TrueBeam LINAC, it became MU factor (left breast, AUC = 0.950) and MCS (right breast, AUC = 0.806), respectively. Overall, there was no universally suitable complexity metric for all types of breast cancers. The choice of complexity metric depended on different cancer types, locations and treatment LINACs. Therefore, when utilizing complexity metrics to predict PSQA outcomes in IMRT for breast cancer, it was essential to select the appropriate metric based on the specific circumstances and characteristics of the treatment.

Implications of Agile Values in Software Engineering for Agility in Breast Cancer Treatment: Protocol for a Comparative Study.

Breast cancer treatment has been described as a dynamic and patient-centered approach that emphasizes adaptability and flexibility throughout the treatment process. Breast cancer is complex, with varying subtypes and stages, making it important to tailor treatment plans to each patient's unique circumstances. Breast cancer treatment delivery relies on a multidisciplinary team of health care professionals who collaborate to provide personalized care and quick adaptation to changing conditions to optimize outcomes while minimizing side effects and maintaining the patient's quality of life. However, agility in breast cancer treatment has not been defined according to common agile values and described in language comprehensible to breast cancer professionals. In the rapidly evolving landscape of breast cancer treatment, the incorporation of agile values from software engineering promises to enhance patient care. Our objective is to propose agile values for breast cancer treatment adopted and adapted from software engineering. We also aim to validate how these values conform to the concept of agility in the breast cancer context through referencing past work. We applied a structured research methodology to identify and validate 4 agile values for breast cancer treatment. In the elicitation phase, through 2 interviews, we identified 4 agile values and described them in language that resonates with breast cancer treatment professionals. The values were then validated by a domain expert and discussed in the context of supporting work from the literature. Final validation entailed a domain expert conducting a walkthrough of the 4 identified agile values to adjust them as per the reported literature. Four agile values were identified for breast cancer treatment, and among them, we validated 3 that conformed to the concept of agility. The fourth value, documentation and the quality of documentation, is vital for breast cancer treatment planning and management. This does not conform to agility. However, its nonagility is vital for the agility of the other values. None of the identified agile values were validated as partially conforming to the concept of agility. This work makes a novel contribution to knowledge in identifying the first set of agile values in breast cancer treatment through multidisciplinary research. Three of these values were evaluated as conforming to the concept of agility, and although 1 value did not meet the concept of agility, it enhanced the agility of the other values. It is anticipated that these 4 agile values can drive oncology practice, strategies, policies, protocols, and procedures to enhance delivery of care. Moreover, the identified values contribute to identifying quality assurance and control practices to assess the concept of agility in oncology practice and breast cancer treatment and adjust corresponding actions. We conclude that breast cancer treatment agile values are not limited to 4. RR1-10.2196/53124.

Radiomics to Differentiate Malignant and Benign Breast Lesions: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis.

Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings. A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67). Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.

Implementation and evaluation of an iterative-based algorithm for automatic beam angle optimization in breast cancer treatment planning

IntroductionA beam angle optimization (BAO) algorithm was developed to evaluate its clinical feasibility and investigate the impact of varying BAO constraints on breast cancer treatment plans. Materials and MethodsA two-part study was designed. In part 1, we retrospectively selected 20 patients treated with radiotherapy after breast-conserving surgery. For each patient, BAO plans were designed using beam angles optimized by the BAO algorithm and the same optimization constraints as manual plans. Dosimetric indices were compared between BAO and manual plans. In part 2, fifteen patients with left breast cancer were included. For each patient, three distinct cardiac constraints (mean heart dose < 5 Gy, 3 Gy or 1 Gy) were established during the BAO process to obtain three optimized beam sets which were marked as BAO_H1, BAO_H3, BAO_H5, respectively. These sets of beams were then utilized under identical IMRT constraints for planning. Comparative analysis was conducted among the three groups of plans. ResultsFor part 1, no significant differences were observed between BAO plans and manual plans in all dosimetric indices, except for ipsilateral lung V5, where BAO plans performed slightly better than manual plans (35.5% ± 5.6% vs 36.9% ± 4.3%, p = 0.034). For part 2, Stricter BAO heart constraints resulted in more perpendicular beams. However, there was no significant difference between BAO_H1, BAO_H3 and BAO_H5 with the same IMRT constraint in the heart dose. Meanwhile, the left lung dose was increased while the right breast and lung doses were decreased with stricter heart constraints in BAO. When mean heart dose < 5 Gy in IMRT constraint, the mean dose to the right lung was decreased from 0.46 Gy for BAO_H5 to 0.33 Gy for BAO_H1 (p = 0.027). ConclusionsThe BAO algorithm can achieve quality plans comparable to manual plans. IMRT constraints dominate the final plan dose, while varying BAO constraints alter the trade-off among structures, providing an additional degree of freedom in planning design.

Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer

Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners were allowed to manually adapt them. Plans were evaluated based on clinical goals and DVH parameters. Thirty-seven of 50plans did fulfill all clinical goals without adjustments. Thirteen of these 37 plans were still adjusted but did not improve mean heart or lung dose. These results leave room for improvement of both the DL model as well as education on clinically relevant adjustments.

HAHNet: a convolutional neural network for HER2 status classification of breast cancer

ObjectiveBreast cancer is a significant health issue for women, and human epidermal growth factor receptor-2 (HER2) plays a crucial role as a vital prognostic and predictive factor. The HER2 status is essential for formulating effective treatment plans for breast cancer. However, the assessment of HER2 status using immunohistochemistry (IHC) is time-consuming and costly. Existing computational methods for evaluating HER2 status have limitations and lack sufficient accuracy. Therefore, there is an urgent need for an improved computational method to better assess HER2 status, which holds significant importance in saving lives and alleviating the burden on pathologists.ResultsThis paper analyzes the characteristics of histological images of breast cancer and proposes a neural network model named HAHNet that combines multi-scale features with attention mechanisms for HER2 status classification. HAHNet directly classifies the HER2 status from hematoxylin and eosin (H&E) stained histological images, reducing additional costs. It achieves superior performance compared to other computational methods.ConclusionsAccording to our experimental results, the proposed HAHNet achieved high performance in classifying the HER2 status of breast cancer using only H&E stained samples. It can be applied in case classification, benefiting the work of pathologists and potentially helping more breast cancer patients.

Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer

BackgroundWhether there is axillary lymph node metastasis is crucial for formulating the treatment plan for breast cancer. Currently, invasive methods are still used for preoperative evaluation of lymph nodes. If non-invasive preoperative evaluation can be achieved, it will effectively improve the treatment plan.ObjectiveConstructed a predict model based on ultrasound examination, which forest axillary lymph node metastasis in breast cancer, and validated this model.MethodPatients admitted to Xiamen First Hospital from April 2018 to August 2021 with complete case data were included in this study. Patients who had undergone breast cancer resection and axillary lymph node dissection or sentinel lymph node biopsy were divided into a training and validation cohort in a 7:3 ratio. In the training cohort, patients were divided into metastatic and non-metastatic groups based on whether axillary lymph nodes had metastasis. The parameters of the two groups were compared, and statistically significant parameters were included in multivariate analysis. Then, a Nomogram model was constructed, named Lymph metastasis predict model (LMPM). Calibration curves, receiver operating curve (ROC), and decision curve analysis (DCA) were plotted between the training and validation cohort, calculate the risk score of each patient, identify the optimal cutoff value, and test the predictive efficacy of LMPM.ResultTwo hundred seventy-three patients were enrolled in final study, the average age 49.7 ± 8.7, training cohort included 191 patients, the diameter of breast cancer, the lymph node peak systolic flow velocity (LNPS) and the cortex area hilum ratio (CH) of lymph node were exist significant difference in metastatic and non-metastatic group. Multivariate analysis showed cancer diameter, LNPS and CH included in LMPM, the cutoff value was 95, the calibration curve, ROC, DCA in training and validation cohort show satisfactory result.ConclusionThe predict model-LMPM, can predict axillary lymph node metastasis in breast cancer, which is useful for developing personalized treatment plans. However, further validation of the model is required by incorporating a larger number of patients.

Bioinformatics Analysis of the Genetic and Epigenetic Alterations of Bone Morphogenetic Protein Receptors in Metastatic Breast Cancer.

The leading cause of mortality in patients with breast cancer is metastasis, and bone morphogenetic protein (BMP) signaling activation regulates metastasis in breast cancer. This study explored the genetic and epigenetic modification of BMP receptor genes associated with metastatic breast cancer cells using bioinformatics. The genetic and epigenetic alterations of BMP receptors (BMPR1A, BMPR1B, BMPR2, ACVR2A, ACVR1, ACVR2B, ACVR1B, HJV, and ENG) were examined using cBioportal and methSurv, respectively. mRNA expression was analyzed using TNM plot and bcgenex, and protein expression was studied using Human Protein Atlas. Prognostic value and ROC were investigated using Kaplan-Meier (KM) and ROC plot, respectively. Finally, mutant function was predicted using several databases, including PolyPhen-2, FATHMM, Mutation Assessor, and PredictSNP. Oncoprint analysis showed genetic alterations in BMPR1A (39%), BMPR1B (13%), BMPR2 (34%), ACVR2A (14%), ACVR1 (7%), ACVR2B (13), ACVR1B (35%), HJV (40%), and ENG (33%) across the patients with breast cancer in The Metastatic Breast Cancer Project. The mRNA and protein levels of BMPR2 were increased in metastatic breast tumor tissues compared with those in normal and breast tumor tissues. BMPR1A and BMPR2 showed the highest and lowest levels of epigenetic alterations among the BMP receptors, respectively. The patients with breast cancer who had low levels of BMPR2 had a better overall survival (OS) than those with high levels of BMPR2. Functional mutation prediction showed that mutants in BMPR2 (R272L, E274K, and L685F), ACVR2A (S127L), and ACVR1B (R484H), are deleterious, probably damaging, and possess a cancer phenotype. ROC plot revealed no BMP receptors correlated with endocrine therapy sensitivity. BMPR1B, BMPR2, and ACVR2A levels were significantly linked as moderate prediction of anti-HER2, BMPR2, and ACVR1B demonstrated moderate predictive potential for chemotherapy sensitivity. This study contributed in fully comprehending the significance of genetic and epigenetic alterations in BMP receptors and BMP signaling in metastatic breast cancer cells for the development of breast cancer treatment plans.

Breast Cancer and Obesity: a Qualitative Analysis of a Diverse Population of Breast Cancer Patients' Perspectives on Weight Management.

Obesity and weight gain in breast cancer survivors leads to a greater risk of recurrence and a decreased chance of survival. A paucity of data exists regarding strengths, weaknesses, and barriers for implementing culturally sensitive, patient-centered interventions for weight management among minority communities. The objective of this study was to evaluate breast cancer patients' experience and perspectives regarding weight management in a racially diverse population. Semi-structured qualitative interviews were conducted with breast cancer patients with a body mass index ≥ 25kg/m2 regarding their experience with weight management. Interviews were transcribed verbatim, and a thematic analysis was conducted. Participants (n = 17) most commonly self-identified as non-Hispanic Black (70.6%). Nearly all participants felt comfortable being approached about weight management, yet less than half (41.2%) reported that they knew about the link between breast cancer and body weight prior to the interview. Four themes emerged: (1) lack of knowledge regarding the link between body weight and breast cancer risk, (2) barriers to weight management including family stressors, high cost, mental health issues, and chronic medical conditions, (3) previous attempts at weight loss including bariatric surgery, and (4) best practices for approaching weight management including discussion of weight management prior to survivorship. There is a need for a multidisciplinary, patient-centered weight management program for minority breast cancer patients that improves awareness of the link between weight and breast cancer risk. Weight management should be introduced early on as an element of the treatment plan for breast cancer.

A Dosimetric and Radiobiological Comparison of Intensity Modulated Radiotherapy, Volumetric Modulated Arc Therapy and Helical Tomotherapy Planning Techniques in Synchronous Bilateral Breast Cancer

The present investigation intends to identify the optimal radiotherapy treatment plan for synchronous bilateral breast cancer (SBBC) using dosimetric and radiobiological indexes for three techniques, namely, helical tomotherapy (HT), volumetric modulated arc therapy (VMAT), and intensity-modulated radiotherapy (IMRT). Twenty SBBC treated female patients treatment planning data (average age of 52.5 years) were used as the sample for the present study. Three different plans were created using 50 Gy in a 25 fraction dose regime. Poisson, Niemierko, and LKB models were applied for calculating normal tissue complication probability (NTCP) and tumour control probability (TCP). The target average dose comparison between IMRT with HT and VMAT with HT was highly substantial (P=0.001). The percentage of TCP for IMRT, VMAT, and HT in the Poisson model were 93.70±0.28, 94.68±0.30, and 94.34±0.57, respectively (p<0.05). The dose maximum was lower for the whole lung in the HT plan, with an average dose of 49.31Gy±3.9 (p<0.009). The NTCP values of both Niemierko and LKB models were lower for the heart, lungs, and liver for the IMRT plan. The sparing of organs at risk was higher in the HT plan dosimetrically, and the TCP was higher in the three techniques. The comparison between the three techniques shows that the IMRT and HT techniques could be considered for treating SBBC.

Unmet Care Needs and Uncertainty in Patients Newly Diagnosed with Breast Cancer.

Breast cancer is the most common cancer in women in the world. Statistics revealed the number of breast cancer patients less than 40 years of age increased over time. Clinical studies found there is a trend of yearly illness happening in younger patients, whose needs related to the illness are different from older patients. The purpose of this study was to explore the uncertainty and needs of patients in different age groups who were newly diagnosed with breast cancer. A descriptive cross-sectional survey study was adopted to recruit 128 patients. The Mishel's Uncertainty in Illness Scale (MUIS, Chinese version) and the Cancer Needs Questionnaire (short-form, CNQ-SF) were used to collect data. The results revealed that a patient's age and religion negatively correlated with illness uncertainty. A patient's age, educational level, work or not, and children's age correlated with needs for care. Age, illness uncertainty, and educational level were important predictors of the needs of care, with an explanatory power of 29.0%. In Conclusion, patients ≤40 years of age had greater illness uncertainty and needs for care than those who were &gt;40 years of age. For younger patients newly diagnosed with cancer, medical professionals should take the initiative to provide detailed and complete information on breast cancer treatment plans, prognosis, and home self-care, which helps clarify the possible future treatments and results and further enables patients to acquire self-care skills and knowledge.

MRNA Biomarkers for Invasive Breast Cancer based on a Deep Feature Selection Approach

Early detection of breast cancer and effective identification of its correct stage remain major challenges for healthcare professionals. Testing the tumour for Oestrogen Receptor and Progesterone Receptor is a standard part of the initial evaluation of breast cancer diagnosis and treatment planning. Several expression profiling studies have illustrated that the expression of these hormone receptors is linked with diverse genetic variations, which means that several mutated genes can a affect the development and progression of breast cancer and contribute to its heterogeneity. Unfortunately, due to the high dimensionality and low sample size nature of microarray data, traditional statistical feature selection techniques fail to identify genes that could act as risk factors for breast cancer. Inspired by this, we developed a deep learning-based feature extraction module with a weight interpretation method to select a subset of robust biomarkers across three different mRNA expression data sets from The Cancer Genome Atlas program (TCGA). For a discovered feature (a gene) to be accepted for further investigation, it must have been independently selected by the weight interpretation method from each of the deep feature extraction modules (each having been trained on a different data set). The small panel of discovered biomarkers was then subsequently evaluated using a range of classifiers to ascertain their predictive ability with respect to the above hormone receptor status. We observed strong evidence that the upregulation in the expression levels of highly positively weighted genes within the deep feature selection modules and the down regulation in the expression levels of the highly negatively weighted genes both indicated the strong likelihood of a patient experiencing ER+/PR+ invasive breast cancer. In addition, we discovered a number of potentially novel biomarkers worthy of further consideration.

161P MammaPrint and BluePrint diagnostic tests can be robustly assessed on Whole-Transcriptome NGS platform

161P MammaPrint and BluePrint diagnostic tests can be robustly assessed on Whole-Transcriptome NGS platform

THE INCIDENCE OF RADIATION PNEUMONITIS IN BREAST CANCER PATIENTS WHO RECEIVED RADIOTHERAPY USING 3-DIMENSIONAL TECHNIQUES

Background: According to the 2018 Global Cancer Observatory from the World Health Organization (WHO), breast cancer is the most common cancer in Indonesia. 3-dimensional conformal radiotherapy (3D-CRT) is one of the breast cancer treatment planning. Effect of radiation on the lungs can cause radiation pneumonitis. The incidence rate of pneumonitis in breast cancer patients who received radiotherapy using 3D-CRT techniques from several researchers is still varied and there is no accurate data yet at RSUP Dr. Kariadi.Objective: To determine the incidence of pneumonitis in breast cancer patients undergoing breast cancer 3D-CRT.Methods: The research design used in this study was cross sectional. The subject is chosen by consecutive sampling methods. The independent variables of this study was 3D breast cancer radiation therapy, while the dependent variable was radiation pneumonitis. This hypothesis test on this study is analyzed with chi-square test.Results: Forty one subjects were included in this study, two subjects were found with a picture of pneumonitis on chest radiographs. The incidence of radiation pneumonitis in breast cancer patients who received radiotherapy using 3-dimensional techniques was 4.9%. There is no significant relationship between breast cancer radiation therapy 3-dimensional techniques on the incidence of pneumonitis. And there was no significant difference in the proportion of pneumonitis in patients with breast cancer locations on the right and left sides.Conclusion: The incidence of radiation pneumonitis in breast cancer patients who received radiotherapy using a 3-dimensional technique is 4.9%. Keywords: 3 Dimensional Technique Radiotherapy, Breast Cancer, Radiation Pneumonitis

Impact of Different Beam Energies on The Incidence of Thyroid Cancer in Breast Cancer Radiotherapy

Purpose: During breast radiotherapy, the organs which are located out of the radiation field such as the thyroid are prone to secondary cancers. The present study aims to evaluate the risk of thyroid cancer in breast cancer radiotherapy in conventional and conformal radiation therapy.&#x0D; Materials and Methods: The data related to the thyroid dose in radiotherapy of breast cancer from the study by Behmadi et al. were used. In their study, the thyroid dose was measured on the Alderson RANDO phantom for four different breast cancer treatment plans and two photon energies. Using the Biological Effects of Ionizing Radiation (BEIR) VII model, the risk of thyroid cancer was estimated in conventional and conformal plans with two photon energies (6 and 15 MV) in breast cancer radiotherapy.&#x0D; Results: The Lifetime Attributable Risk (LAR) for thyroid cancer in the conventional technique was only 7.5% higher than that in the conformal technique. In the conventional treatment technique, LAR for thyroid cancer at 6 MV in all age groups was 17% higher than the 15 MV energy. However, the LAR for thyroid cancer in conformal technique at 15 MV energy was 50% higher than at 6 MV energy.&#x0D; Conclusion: Applying high energy for radiotherapy of breast cancer, in the conventional technique, could reduce the risk of thyroid cancer. But at high energies, the risk of thyroid cancer in the conformal technique is considerably higher than that at low energy. Therefore, it is suggested that the impact of energies be evaluated to reduce the risk of thyroid cancer in breast cancer radiotherapy.