Faculty Bibliography

Positron emission tomography / magnetic resonance imaging (PET/MRI) is an emerging imaging technology that allows for the acquisition of multiple MRI parameters simultaneously with PET data. In this review, we address the technical requirements of PET/MRI including protocols and tracers, the potential of integrated localized breast PET/MRI exams, and possible applications of whole-body PET/MRI in breast cancer patients. Currently, PET/MRI can be performed on sequential and integrated PET/MRI scanners but, as not all practices can access these dedicated machines, several studies look at PET and MRI exams that are performed separately on separate scanners within a short time frame. This practice likely provides similar clinical data, although exact colocalization for iso-voxel analysis, currently performed only in research, is not possible. In PET/MRI, the MRI sequences are flexible and can be customized according to the aim of the exam. The most commonly used radiotracer is ¹⁸ F-FDG; however, tracers that image hypoxia and drug targets such as estrogen receptors and HER2 are in development and may increase the utility of PET/MRI. For dedicated breast PET/MRI, a potential advantage over standard breast MRI alone may be the complementary sensitivities of MRI for extent of disease within the breast and PET for axillary and internal mammary nodal metastases. Moreover, layers of multiparametric MRI and PET metrics derived from the index lesion are being investigated as predictors of response to neoadjuvant therapy. These data may eventually be able to be quantified and mined in a way that furthers radiomics and also precision medicine. Finally, in whole-body imaging of breast cancer patients, single-institution studies have found that PET/MRI detects more metastases than PET at about half the radiation dose, although a survival benefit has not been shown. For now, whole-body PET/MRI in breast cancer patients may be most relevant for young patients who may undergo serial surveillance exams.

PURPOSE/OBJECTIVE:To investigate how breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography/ magnetic resonance imaging (PET/MRI) in patients with breast cancer is related to background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT), and age, as well as whether BPU varies as a function of distance from the primary breast cancer. MATERIALS AND METHODS/METHODS:volume of interest 1) in the same quadrant of the ipsilateral breast, 5 mm from the index lesion; 2) in the opposite quadrant of the ipsilateral breast; and 3) in contralateral breast, quadrant matched to the opposite quadrant of the ipsilateral breast. The maximum standardized uptake value (SUVmax) of the index cancer was measured using a VOI that included the entire volume of the index lesion. Bleed from the primary tumor was corrected for (PET edge, MIM). FGT and BPE was assessed by 2 readers on a 4-point scale in accordance with BI-RADS lexicon. The Wilcoxon signed rank test and the Spearman rank correlation test were performed. RESULTS:BPU was significantly greater in the same quadrant as the breast cancer as compared with the opposite quadrant of the same breast (p < 0.001 for both readers) and was significantly greater in the opposite quadrant of the same breast compared to the matched quadrant of the contralateral breast (p = 0.002 for reader 1 and <0.001 for reader 2). While the FGT SUVmax in the same quadrant as the cancer correlated significantly with SUVmax of the index lesion, the FGT SUVmax in the opposite quadrant of the same breast and in the matched quadrant of the contralateral breast did not. The FGT SUVmax in the contralateral breast positively correlated with the degree of BPE and negatively correlated with age, but did not show a significant correlation with the amount of FGT for either reader. CONCLUSION/CONCLUSIONS:There appears to be an inverse correlation between metabolic activity of normal breast parenchyma and distance from the index cancer. BPU significantly correlates with BPE.

BACKGROUND:Cancers induce gene expression alterations in stroma surrounding tumors that supports cancer progression. However, it is actually not at all known the extent of altered stromal gene expression enacted by tumors nor the extent to which altered stromal gene expression penetrates the stromal tissue. Presently, post-surgical "tumor-free" stromal tissue is determined to be cancer-free based on solely on morphological normality-a criteria that has not changed in more than 100 years despite the existence of sophisticated gene expression data to the contrary. We therefore investigated the extent to which breast tumors alter stromal gene expression in three dimensions in women undergoing mastectomy with the intent of providing a genomic determination for development of future risk of recurrence criteria, and to inform the need for adjuvant full-breast irradiation. METHODS AND FINDINGS/RESULTS:Genome-wide gene expression changes were determined in histopathologically normal breast tissue in 33 women undergoing mastectomy for stage II and III primary invasive ductal carcinoma at serial distances in three dimensions from the tumor. Gene expression was determined by genome-wide mRNA analysis and subjected to metagene mRNA characterization. Tumor-like gene expression signatures in stroma were identified that surprisingly transitioned to a plastic, normalizing homeostatic signature with distance from tumor. Stroma closest to tumor displayed a pronounced tumor-like signature enriched in cancer-promoting pathways involved in disruption of basement membrane, cell migration and invasion, WNT signaling and angiogenesis. By 2 cm from tumor in all dimensions, stromal tissues were in transition, displaying homeostatic and tumor suppressing gene activity, while also expressing cancer supporting pathways. CONCLUSIONS:The dynamics of gene expression in the post-tumor breast stroma likely co-determines disease outcome: reversion to normality or transition to transformation in morphologically normal tissue. Our stromal genomic signature may be important for personalizing surgical and adjuvant therapeutic decisions and risk of recurrence.

Background/UNASSIGNED:In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report. Methods/UNASSIGNED:A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy. Results/UNASSIGNED:Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%. Conclusions/UNASSIGNED:A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.

DCIS is a non-obligate precursor in which epithelial cells with features of cancer reside in and expand otherwise normal appearing mammary ducts and lobules. DCIS increases the relative risk of developing invasive breast cancer (IBC) by up to 10 fold. Treatment of DCIS includes breast conserving surgery (BCS), tumor margins permitting, followed by radiation, or mastectomy. Recently we found that pasireotide, a somatostatin analog inhibitor of IGF-I, was effective in reducing cell proliferation and increasing apoptosis in patients with atypical hyperplasia (AH) and or proliferative disease of the breast. At present there are no recognized medical treatments for DCIS. Here we raise the possibility that pasireotide might also be an effective therapy for some DCIS. To make this determination, 8 patients previously diagnosed with DCIS were treated with pasireotide, 600mug twice daily subcutaneously for a period of 20 days. Lesion size was assessed on DCE-MRIs in 3 planes before and after treatment. Of the eight, two had lesions characterized as low grade DCIS on core biopsy, 4 had intermediate or intermediate to high grade lesions, and two had high grade lesions. After treatment they all had surgical excision biopsies. We also measured proliferation and apoptosis to assess effects of pasireotide by expression of Ki67 and caspase 3. To confirm that the effect of pasireotide was mediated via the IGF-I pathway, p-AKT and p-ERK 1/2 were evaluated. Core and excision samples from 14 untreated patients where used as control. Overall, treatment with pasireotide reduced DCIS lesion size by >=30% according the RECIST criteria in 4 of 8 patients, as measured by DCE-MRI. Of those, pathology of the surgical excision confirmed that there was no remaining DCIS tissue in the two patients with low grade disease. The two additional lesions that were reduced in size one was intermediate to high grade and the other a high grade DCIS. The remaining 4 patients had tumors that were stable in size. Pasireotide reduced cell proliferation and significantly increased apoptosis in the DCIS lesions. While DCIS is less responsive to pasireotide than AH and proliferative disease, it may nevertheless provide a window of opportunity for treatment and possibly eradication of some low grade or higher grade DCIS as well. The main side effect included hyperglycemia, intestinal discomfort and rash or redness at the injection area. Our data show that hyperglycemia disappears relatively fast after ending treatment with pasireotide. In conclusion, treatment with pasireotide eliminated 2 low grade DCIS lesions, shrunk two higher grade ones and also decreased proliferation and increased apoptosis. Additional evidence in a larger number of patients will be necessary

Background: To examine the trends in clinicopathologic features, treatment, and survival of male breast cancer (MBC), utilizing the National Cancer Data Base (NC