ABSTRACT
PURPOSE: The objective of this study was to assess the inappropriateness rate of oncological follow-up CT examinations. METHODS: Out of 7.000 oncology patients referred for follow-up CT examinations between March and October 2022, a random sample of 10 % was included. Radiology residents assessed the appropriateness using the Italian Society of Medical Oncology (AIOM) guidelines, supervised by senior radiologists. Association between inappropriateness and clinical variables was investigated and variables influencing inappropriateness were analyzed through a binary logistic regression. RESULTS: Three-hundred-eighty-eight examinations (56.1 %) were consistent with AIOM guidelines. An additional 100 (14.5 %) examinations did not follow the recommended schedule but were nevertheless considered appropriate because of suspected recurrence/progression (10.7 %) or adverse event requiring imaging assessment (3.8 %). Two-hundred-four (29.4 %) examinations were rated as inappropriate. Inappropriateness causes were as follows: CT not included in the relevant guideline (n = 47); CT extended to additional anatomical regions (n = 59); CT requested at a shorter time-interval (n = 98). No statistically significant difference was found in age, sex, scan region, and primary cancer between appropriate and inappropriate examinations. The only variable significantly associated with inappropriateness was being referred by a specific hospital unit named "unit 2" in the study (p = 0.009), which was demonstrated to be the only appropriateness independent predictor (OR 1.952). CONCLUSION: This study shows that majority of oncological patients referred for follow-up CT follows standard guidelines. However, a non-negligible proportion was rated as inappropriate, mainly due to the shorter time-interval. No clinical variable was associated with inappropriateness, except for referral by a specific hospital unit.
Subject(s)
Medical Oncology , Physical Examination , Humans , Cross-Sectional Studies , Follow-Up Studies , Tomography, X-Ray ComputedABSTRACT
Background: Structured reporting has been demonstrated to increase report completeness and to reduce error rate, also enabling data mining of radiological reports. Still, structured reporting is perceived by radiologists as a fragmented reporting style, limiting their freedom of expression. Purpose: A deep learning-based natural language processing method was developed to automatically convert unstructured COVID-19 chest CT reports into structured reports. Methods: Two hundred-two COVID-19 chest CT were retrospectively reviewed by two experienced radiologists, who wrote for each exam a free-form text radiological report and coherently filled the template provided by the Italian Society of Medical and Interventional Radiology, used as ground-truth. A semi-supervised convolutional neural network was implemented to extract 62 categorical variables from the report. Two iterations were carried-out, the first without fine-tuning, the second one performing a fine-tuning. The performance was measured using the mean accuracy and the F1 mean score. An error analysis was performed to identify errors entirely attributable to incorrect processing of the model. Results: The algorithm achieved a mean accuracy of 93.7% and an F1 score 93.8% in the first iteration. Most of the errors were exclusively attributable to wrong inference (46%). In the second iteration the model achieved for both parameters 95,8% and percentage of errors attributable to wrong inference decreased to 26%. Conclusions: The convolutional neural network achieved an optimal performance in the automated conversion of free-form text into structured radiological reports, overcoming all the limitation attributed to structured reporting and finally paving the way for data mining of radiological report.
ABSTRACT
Malignant pleural mesothelioma is a rare neoplasm with poor prognosis. CT is the first imaging technique used for diagnosis, staging, and assessment of therapy response. Although, CT has intrinsic limitations due to low soft tissue contrast and the current staging system as well as criteria for evaluating response, it does not consider the complex growth pattern of this tumor. Computer-based methods have proven their potentiality in diagnosis, staging, prognosis, and assessment of therapy response; moreover, computer-based methods can make feasible tasks like segmentation that would otherwise be impracticable. MRI, thanks to its high soft tissue contrast evaluation of contrast enhancement and through diffusion-weighted-images, could replace CT in many clinical settings.
