The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Transfer learning for non-image data in clinical research: A scoping review

Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research

Transfer learning for non-image data in clinical research: A scoping review.

BackgroundTransfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature.Methods and findingsWe systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%).ConclusionsIn this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research

[18F]FDOPA PET/CT is superior to [68Ga]DOTATOC PET/CT in diagnostic imaging of pheochromocytoma

Abstract Background Both [18F]FDOPA (FDOPA) and [68Ga]DOTATOC PET/CT (DOTATOC) are widely used for detection of pheochromocytomas/paraganglioma (PPGL). However, direct comparisons of the performance of the two tracers are only available in small series. We conducted a retrospective comparative analysis of FDOPA and DOTATOC to assess their sensitivity and accuracy in detecting PPGL when administered based on suspicion of PPGL. We consecutively included patients referred on suspicion of PPGL or PPGL recurrence who were scanned with both FDOPA and DOTATOC. Both scans were reviewed retrospectively by two experienced observers, who were blinded to the final diagnosis. The assessment was made both visually and quantitatively. The final diagnosis was primarily based on pathology. Results In total, 113 patients were included (97 suspected of primary PPGL and 16 suspected of recurrence). Of the 97 patients, 51 had pheochromocytomas (PCC) (in total 55 lesions) and 6 had paragangliomas (PGL) (in total 7 lesions). FDOPA detected and correctly localized all 55 PCC, while DOTATOC only detected 25 (sensitivity 100% vs. 49%, p < 0.0001; specificity 95% vs. 98%, p = 1.00). The negative predictive value (100% vs. 63%, p < 0.001) and diagnostic accuracy (98% vs. 70%, p < 0.01) were higher for FDOPA compared to DOTATOC. FDOPA identified 6 of 6 patients with hormone producing PGL, of which one was negative on DOTATOC. Diagnostic performances of FDOPA and DOTATOC were similar in the 16 patients with previous PPGL suspected of recurrence. Conclusions FDOPA is superior to DOTATOC for localization of PCC. In contrast to DOTATOC, FDOPA also identified all PGL but with a limited number of patient cases

Epidemiology of adrenal tumours in Olmsted County, Minnesota, USA: a population-based cohort study.

BACKGROUND Adrenal tumours are commonly encountered in clinical practice, but epidemiological data mainly originate from referral centres. We aimed to determine incidence, prevalence, and rates of malignancy and hormone excess in patients with adrenal tumours in a standardised geographically well defined population. METHODS In this retrospective population-based cohort study we assessed the standardised incidence rate of adrenal tumours in all patients with tumours who lived in Olmsted County, MN, USA, from Jan 1, 1995, to Dec 31, 2017. The Rochester Epidemiology Project infrastructure, which links medical records across all health-care providers for the entire population of Olmsted County since 1966, was used to allow researchers to identify individuals with specific diagnoses, surgical interventions, and other procedures, and to locate their medical records, which were then used in the analysis. Incidence rates and prevalence were standardised for age and sex according to the 2010 US Population. FINDINGS An adrenal tumour was diagnosed in 1287 patients (median age 62 years; 713 (55·4%) were women; and 13 (1·0%) were children). Standardised incidence rates increased from 4·4 (95% CI 0·3-8·6) per 100 000 person-years in 1995 to 47·8 (36·9-58·7) in 2017, mainly because of the incidental discovery of adenomas less than 40 mm in diameter in patients older than 40 years. Prevalence of adrenal tumours in 2017 was 532 per 100 000 inhabitants, ranging from 13 per 100 000 in children (aged <18 years) to 1900 per 100 000 in patients older than 65 years. 111 (8·6%) of 1287 patients were diagnosed with malignancy (96 [7·5%] of whom has metastases), 14 (1·1%) with phaeochromocytoma, and 53 (4·1%) with overt steroid hormone excess. Malignancy was more common in children (62%) versus those older than 18 years (8%; p<0·0001), tumours discovered during cancer-staging or follow-up (43% vs 3% for incidentalomas; p<0·0001), tumours more than 40 mm in diameter (34% vs 6% for tumours <20 mm; p<0·0001), tumours with unenhanced CT attenuation of 30 Hounsfield units or more (20% vs 1% for <20 Hounsfield units; p<·0001), and bilateral masses (16% vs 7% for unilateral, p=0·0004). INTERPRETATION Adrenal tumour standardised incidence rates increased 10 times from 1995 to 2017. Population-based data revealed lower rates of malignancy, phaeochromocytoma, and overt steroid hormone excess than previously reported. FUNDING National Institutes of Health