Large cell neuroendocrine carcinomas (LCNEC) are rare, aggressive high-grade neuroendocrine neoplasms within the neuroendocrine cell lineage spectrum. This manuscript provides a detailed review of published literature on LCNEC of gynecological origin. We performed a PubMed search for material available on gynecologic LCNEC. We analyzed 104 unique cases of gynecologic LCNECs, of which 45 were cervical primary, 45 were ovarian, 13 were uterine, and 1 was vaginal. A total of 45 cases of cervical LCNEC were identified with a median age of 36 years. Median overall survival was 16 months. We identified 45 ovarian LCNEC cases in the published literature with a median age of 54 years. Median overall survival was 8 months. 13 LCNEC cases of uterine origin were identified; 12 out of 13 were of endometrial origin and the median age was 71 years. The majority of patients presented with Stage III/IV disease (stages I–IV were 31%, 8%, 38%, and 23%, respectively). Gynecologic LCNEC is an aggressive malignancy. Our current understanding of the disease biology is very limited. Efforts are required to better understand the genomic and molecular characterizations of gynecological LCNEC. These efforts will elucidate the underlying oncogenic pathways and driver mutations as potential targets.
Gallium-68 DOTATATE provides physiologic imaging and assists in disease localization for somatostatin receptor (SSTR) positive neuroendocrine tumor (NET) patients. However, questions regarding usefulness of gallium-68 DOTATATE imaging in identifying the primary site in neuroendocrine tumors (NETS) of unknown primary, correlation of NET grade with median Standardized Uptake Value (SUV) and effects of long acting somatostatin analog on gallium-68 DOTATATE imaging quality needs to be evaluated. A single institution retrospective review of the first 200 NET patients with gallium-68 DOTATATE imaging from Dec 2016 to Dec 2017 was conducted. Questions related to NETs of unknown primary, correlation of Standardized Uptake Value (SUV) to Ki-67 (which signifies proliferation rate), the effects of long-acting systemic somatostatin analog (SSA) on SUV were part of our data analysis. From these 200 patients, 59.5% (119) were females, 40.5% (81) were males; the median age was 62 years. The following primary tumor sites were identified: small bowel-37.5%; pancreas-18.5%; bronchial-14%; colon-3.5%; rectum-2%; appendix-1.5%; adrenal-0.5%; prostate-0.5%; others-3% and unknown primary-19%. Mean hepatic SUV of the lesion with the greatest radiolabeled uptake in 96 patients was similar irrespective to exposure to long acting SSA. Patients exposed to long acting SSA had mean SUV of 31.3 vs 27.8 for SSA naïve patients. The difference was not statistically significant. Gallium-68 DOTATATE imaging seems to distinguished G3 NET from G1/G2 based on mean SUV, and also identified the primary tumor site in 17 of 38 (45%) patients with unknown primary. Systemic exposure to long acting SSA does not appear to influence mean SUV of gallium-68 DOTATATE scan.
In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, the current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge in the patient care pathway. Therefore, an accurate, reliable, and fast computer-aided diagnosis (CAD) system capable of detecting abnormalities in chest X-rays is crucial in improving the radiological workflow. In this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. Methods: A team of radiologists used the AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multicentre study. This study took place in a large radiology network in India between June 2021 and March 2022. Results: A total of 65,604 chest X-rays were processed during the study period. The overall performance of AI achieved in detecting normal and abnormal chest X-rays was good. The high negatively predicted value (NPV) of 98.9% was achieved. The AI performance in terms of area under the curve (AUC), NPV for the corresponding subabnormalities obtained were blunted CP angle (0.97, 99.5%), hilar dysmorphism (0.86, 99.9%), cardiomegaly (0.96, 99.7%), reticulonodular pattern (0.91, 99.9%), rib fracture (0.98, 99.9%), scoliosis (0.98, 99.9%), atelectasis (0.96, 99.9%), calcification (0.96, 99.7%), consolidation (0.95, 99.6%), emphysema (0.96, 99.9%), fibrosis (0.95, 99.7%), nodule (0.91, 99.8%), opacity (0.92, 99.2%), pleural effusion (0.97, 99.7%), and pneumothorax (0.99, 99.9%). Additionally, the turnaround time (TAT) decreased by about 40.63% from pre-qXR period to post-qXR period. Conclusion: The AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.
632 Background: Incidence and prevalence of neuroendocrine tumors (NETs) are on the rise. However, with the increasing burden of the disease, there is a need to understand financial toxicity associated with NET management. Especially the out of pocket spending or expenses made by patients and family members, which affects the management and treatment adherence. We report results of our prospective online survey based study looking into determinants and the burden of financial toxicity in NET patients. Methods: We collaborated with NCAN (Neuroendocrine Cancer Awareness Network) a national NET patient support group for our prospective online questionnaire-based survey. After IRB approval, NCAN disseminated online link to the survey on their webpage. The survey had twenty-one questions with the goal of at least 200 complete responses. Results: We received 215 responses before we decided to stop the survey for analysis. The average age of the respondents was 58.4 years. 75% of respondents were females with most repsondents of the survey (37%) being graduates. About 41% of respondents had small bowel NET while 27% had pancreas NET as their NET diagnosis. The median income of the respondents was $65000/year. Median oncology visits were reported to be 4 per year. Median co-pay for the NET specialist visit was found to be $30. The median spending on travel for the physician`s appointment was $250 per visit to see NET specialists. Median monthly insurance premium was $300. Study participants reported spending $75 out of pocket on NET related care every month on top of insurance premiums. More than half of the respondents were on Somatostatin analogues only as their main treatment. About 21% mentioned that they were not able to get required treatment due to cost related issues. Conclusions: The preliminary analysis of the survey gave us an insight into financial toxicity related to NET management. Although most NETs are indolent, the chronic nature of disease and associated, symptom management is often time and resource-intensive. Many patients mentioned having issues with the coverage of insurance and having to travel a long distance to see a NET specialist, which demonstrate a lack of access to NET specialists.
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