Semi-automatic tumor delineation for evaluation of 64Cu-DOTATATE PET/CT in patients with neuroendocrine neoplasms:prognostication based on lowest lesion uptake and total tumor volume

Patients with neuroendocrine neoplasms (NENs) have heterogeneous somatostatin receptor expression, with highly differentiated lesions having higher expression. Receptor expression of the total tumor burden may be visualized by somatostatin receptor imaging, such as with (64)Cu-DOTATATE PET/CT. Assessment of maximal lesion uptake is associated with progression-free survival (PFS) but not overall survival (OS). We hypothesized that the lesion with the lowest, rather than the highest, (64)Cu-DOTATATE uptake would be more prognostic, and we developed a semiautomatic method for evaluating this hypothesis. Methods: Patients with NENs underwent (64)Cu-DOTATATE PET/CT. A standardized semiautomatic tumor delineation method was developed and used to identify the lesion with the lowest uptake, that is, with the lowest SUV(mean). Additionally, we assessed total tumor volume derived from the semiautomatic tumor delineation. Kaplan–Meier and Cox regression analyses were used to determine whether there was any association with OS and PFS. Results: In 116 patients with NENs, median PFS (95% CI) was 23 mo (range, 20–31 mo) and median OS was 85 mo (range, 68–113 mo). Minimum SUV(mean) and total tumor volume were significantly associated with PFS and OS in univariate Cox regression analyses, whereas SUV(max) was significant only for PFS. In multivariate Cox analyses, both minimum SUV(mean) and total tumor volume remained statistically significant. Minimum SUV(mean) and total tumor volume were then dichotomized by their median, and patients were categorized into 4 groups: high or low total tumor volume and high or low minimum SUV(mean). Patients with a low total tumor volume and high minimum SUV(mean) had a hazard ratio of 0.32 (95% CI, 0.20–0.51) for PFS and 0.24 (95% CI, 0.13–0.43) for OS, both with P values of less than 0.001 (reference: high total tumor volume and low minimum SUV(mean)). Conclusion: We propose a standardized semiautomatic tumor delineation method to identify the lesion with the lowest (64)Cu-DOTATATE uptake and total tumor volume. Assessment of the lowest, rather than the highest, lesion uptake greatly increases prognostication by (64)Cu-DOTATATE PET/CT. Combining lesion uptake and total tumor volume, we derived a novel prognostic classification system for patients with NENs

Phase II study of a 3-day schedule with topotecan and cisplatin in patients with previously untreated small cell lung cancer and extensive disease

IntroductionTreatment with a topoisomerase I inhibitor in combination with a platinum results in superior or equal survival compared with etoposide-based treatment in extensive disease small cell lung cancer (SCLC). Five-day topotecan is inconvenient and therefore shorter schedules of topotecan and cisplatin are needed. The aim of this phase II study was to establish the response rate and response duration in chemo-naive patients with SCLC receiving a 3-day topotecan and cisplatin schedule.MethodsSimons optimal two-stage design was used. Patients with previously untreated extensive disease SCLC, adequate organ functions and performance status less than 3 were eligible. Topotecan (2.0 mg/m2, intravenously) was administered on days 1 to 3 with cisplatin (50 mg/m2, intravenously) on day 3 every 3 weeks for a total of six cycles.ResultsForty-three patients received 219 cycles of chemotherapy. Median age was 59 (range 44–74), 79% had performance status 0 or 1. Thirty-one patients completed all six cycles. Grade 3/4 anemia, neutrocytopenia, and thrombocytopenia were recorded in 9.5%, 66.7%, and 21.4% of patients, respectively. Fourteen percent of patients experienced neutropenic fever. No episodes of fatal sepsis occurred. Non-hematologic toxicity was mild and manageable. Overall and complete response rates were 72.1% and 9.3%, respectively. The median overall survival and response duration were 10.3 months (95% confidence interval: 8.6–12.0) and 7.0 months (95% confidence interval: 6.3–7.7), respectively.ConclusionThree-day topotecan with cisplatin on day 3 is active and safe in extensive disease SCLC. An ongoing phase III randomized trial compares this combination to standard treatment

A convolutional neural network for total tumor segmentation in [⁶⁴Cu]Cu-DOTATATE PET/CT of patients with neuroendocrine neoplasms

BACKGROUND: Segmentation of neuroendocrine neoplasms (NENs) in [(64)Cu]Cu-DOTATATE positron emission tomography makes it possible to extract quantitative measures useable for prognostication of patients. However, manual tumor segmentation is cumbersome and time-consuming. Therefore, we aimed to implement and test an artificial intelligence (AI) network for tumor segmentation. Patients with gastroenteropancreatic or lung NEN with [(64)Cu]Cu-DOTATATE PET/CT performed were included in our training (n = 117) and test cohort (n = 41). Further, 10 patients with no signs of NEN were included as negative controls. Ground truth segmentations were obtained by a standardized semiautomatic method for tumor segmentation by a physician. The nnU-Net framework was used to set up a deep learning U-net architecture. Dice score, sensitivity and precision were used for selection of the final model. AI segmentations were implemented in a clinical imaging viewer where a physician evaluated performance and performed manual adjustments. RESULTS: Cross-validation training was used to generate models and an ensemble model. The ensemble model performed best overall with a lesion-wise dice of 0.850 and pixel-wise dice, precision and sensitivity of 0.801, 0.786 and 0.872, respectively. Performance of the ensemble model was acceptable with some degree of manual adjustment in 35/41 (85%) patients. Final tumor segmentation could be obtained from the AI model with manual adjustments in 5 min versus 17 min for ground truth method, p < 0.01. CONCLUSION: We implemented and validated an AI model that achieved a high similarity with ground truth segmentation and resulted in faster tumor segmentation. With AI, total tumor segmentation may become feasible in the clinical routine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-022-00901-2