Identifying gaps in the coverage of survivorship care services.

6583 Background: Despite advancements in reimbursement, anecdotal evidence suggests patients are not able to access guideline concordant survivorship care services due to a lack of coverage by payers. We present the results of a mixed methods study aimed to determine the practice-reported rates and sources of delay/denial on evidence-based, guideline concordant survivorship care services. Methods: A quantitative survey was developed by ASCO’s Cancer Survivorship Committee (CSC) to assess which services are being denied by payers for coverage/reimbursement. Questions were limited to disease sites for which practice guidelines exist. 533 ASCO members who provide survivorship care were surveyed, with a focus on obtaining representation from rural/urban, academic/private practice, pediatric/adult, and geographic location across the U.S. Semi-structured telephone interviews were conducted in October and November 2020 with geographic sub sample representation to further explore the nature of and extent to which coverage barriers are experienced for guideline-concordant care, specific to the provider or clinic’s primary disease site or specialty. Results: 120 responses from 50 states were included. Respondents were primarily clinicians (88%) with the majority treating patients with Medicare/Medicaid/CHIP (60%), followed by private/employer insurance (38%). There was little issue with coverage of hormone therapies. One-third reported issues some of the time with maintenance chemotherapy (38%) and immunotherapy (35%). Coverage denials for screening for recurrence for breast cancer (MRI, 63.5%), Hodgkin Lymphoma (PET/CT 47%; Breast MRI, 44.4%), and lung cancer (Low-dose CT 37.4%) were common. Half of the survey respondents reported denials for supportive care/symptom management services (Table). Private or employer-based insurance denials were most often the source of barriers (57.7%). Through interviews, denials were found to be the same across sites and not unique to a single payer or region. Most had a process to appeal denials for evidence-based services. Conclusions: Denial for survivorship care, particularly supportive care services, is common. There is a need for better advocacy with payers, improved policy, and support for providers/practices to implement protocols to obtain coverage for services, particularly in the face of burnout.[Table: see text]

Spontaneous regression of breast carcinoma: A radio-pathological case report and review of the literature

Spontaneous regression (SR) is defined as complete or partial disappearance of a proven malignant tumor without adequate medical treatment. Although there have been reports on SR of breast cancers, this phenomenon remains rare. We report a case of SR of breast cancer in a 62-year-old woman who presented to our hospital with a few months history of a painless breast lump. Initial radiological investigations reveal a suspicious mass in the left upper outer quadrant. Histopathology confirms the presence of invasive cancer. The patient initially refused any surgical or medical interventional and defaulted follow-up. The patient presented again after four months with consent for surgical treatment. Repeat ultrasound just before the hook wire localization procedure demonstrated the absence of tumor in the region of interest. The subsequent repeat mammogram and MRI also demonstrated complete regression of breast cancer. MRI however, does reveal a suspicious left axillary lymph node, which was also deemed suspicious on PET-CT. In view of these findings, the patient was counsel for surgery. The exact mechanism of SR of cancer remains unclear in our patient. We are unable to identify the exact mechanism triggering and influencing the SR in our patient. Our hypotheses include substance within the herbal remedies or a carcinoma-directed immune response triggered by the biopsy. Further research is needed to determine causes for spontaneous regressions of cancer and towards finding a possible cure for cancer.

Novel Supervised Learning Scheme for Optimizing the Classification Performance of Breast Cancer MRI

Usage of machine learning has been always proven potential in identifying the best solution from the set of complex variables with the highly inter-twined relationship of problems. Similarly, supervised learning approach is one essential operation under machine learning that has always contributed in the area of healthcare and diagnostics. However, there are still some problems associated with the detection and classification of complex disease condition that is yet to be solved. The proposed system introduces a novel supervised learning approach along with a novel feature extraction scheme which is more progressive and less iterative. The proposed system considers a case study to perform classification of breast cancer using Magnetic Resonance Imaging (MRI) where it is subjected to normalization first followed by a novel segmentation process that compliments the classification operation too. The study outcome shows that the proposed system offers better classification performance in contrast to existing supervised approaches.

Stereotatic Radiosurgery and Microsurgery for Brain Metastases

This chapter describes the case of a patient with brain metastases due to metastatic breast cancer. MRI is the best imaging modality for visualizing brain metastases, and advanced techniques such as perfusion imaging and diffusion weighted imaging may provide important additional information beyond standard anatomic imaging. Patients with brain metastases due to systemic cancer may benefit from targeted therapies such as surgery and stereotactic radiosurgery. Understanding the differences between radiation modalities such as stereotactic radiosurgery and whole brain radiotherapy is important for counseling patients.

Segmentasi Citra Mri Menggunakan Deteksi Tepi Untuk Identifikasi Kanker Payudara

One type of cancer that is capable identified using MRI technology is breast cancer. Breast cancer is still the leading cause of death world. therefore early detection of this disease is needed. In identifying breast cancer, a doctor or radiologist analyzing the results of magnetic resonance image that is stored in the format of the Digital Imaging Communication In Medicine (DICOM). It takes skill and experience sufficient for diagnosis is appropriate, andaccurate, so it is necessary to create a digital image processing applications by utilizing the process of object segmentation and edge detection to assist the physician or radiologist in identifying breast cancer. MRI image segmentation using edge detection to identification of breast cancer using a method stages gryascale change the image format, then the binary image thresholding and edge detection process using the latest Robert operator. Of the20 tested the input image to produce images with the appearance of the boundary line of each region or object that is visible and there are no edges are cut off, with the average computation time less than one minute.