Perceived misinterpretation rates in oncologic 18F-FDG PET/CT studies: a survey of referring physicians

Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation

OBJECTIVES

To develop and test a Retina U-Net algorithm for the detection of primary lung tumors and associated metastases of all stages on FDG-PET/CT.

METHODS

A data set consisting of 364 FDG-PET/CTs of patients with histologically confirmed lung cancer was used for algorithm development and internal testing. The data set comprised tumors of all stages. All lung tumors (T), lymphatic metastases (N), and distant metastases (M) were manually segmented as 3D volumes using whole-body PET/CT series. The data set was split into a training (n = 216), validation (n = 74), and internal test data set (n = 74). Detection performance for all lesion types at multiple classifier thresholds was evaluated and false-positive-findings-per-case (FP/c) calculated. Next, detected lesions were assigned to categories T, N, or M using an automated anatomical region segmentation. Furthermore, reasons for FPs were visually assessed and analyzed. Finally, performance was tested on 20 PET/CTs from another institution.

RESULTS

Sensitivity for T lesions was 86.2% (95% CI: 77.2-92.7) at a FP/c of 2.0 on the internal test set. The anatomical correlate to most FPs was the physiological activity of bone marrow (16.8%). TNM categorization based on the anatomical region approach was correct in 94.3% of lesions. Performance on the external test set confirmed the good performance of the algorithm (overall detection rate = 88.8% (95% CI: 82.5-93.5%) and FP/c = 2.7).

CONCLUSIONS

Retina U-Nets are a valuable tool for tumor detection tasks on PET/CT and can form the backbone of reading assistance tools in this field. FPs have anatomical correlates that can lead the way to further algorithm improvements. The code is publicly available.

KEY POINTS

• Detection of malignant lesions in PET/CT with Retina U-Net is feasible. • All false-positive findings had anatomical correlates, physiological bone marrow activity being the most prevalent. • Retina U-Nets can build the backbone for tools assisting imaging professionals in lung tumor staging.

Diagnostic errors in clinical FDG-PET/CT

PURPOSE

To determine the frequency, types, and determinants of diagnostic errors in clinical FDG-PET/CT, based on addenda to the original report.

MATERIALS AND METHODS

This retrospective study included 4,099 consecutive clinical FDG-PET/CT scans with corresponding reports that were made at a tertiary care center in an 18-month period. FDG-PET/CT reports were scrutinized for the presence of an addendum enclosing a diagnostic error.

RESULTS

90 of 4,099 FDG-PET/CT reports (2.2%) contained an addendum enclosing a diagnostic error. The distribution of perceptual and cognitive errors among these 90 diagnostic errors was 54 (60.0%)/36 (40.0%). On multivariate logistic regression analysis, only low-dose FDG-PET/CT combined with concomitantly acquired and interpreted full-dose contrast-enhanced CT remained as significantly and independently associated with the presence of a diagnostic error, relative to low-dose FDG-PET/CT without concomitantly acquired and interpreted full-dose contrast-enhanced CT (odds ratio: 2.79 [95% confidence interval: 1.61-4.85], P < 0.001). Patient age, gender, hospital status, indication for FDG-PET/CT scanning, single vs. double reading (i.e. two medical imaging specialists), reader experience, and reading by a nuclear medicine physician only vs. reading by both a nuclear medicine physician and a radiologist, were not significantly and independently associated with the presence of a diagnostic error.

CONCLUSION

Diagnostic errors in clinical FDG-PET/CT based on addenda to the original report are relatively infrequent, though certainly non-negligible. Perceptual errors are slightly more frequent than cognitive errors. The availability of a concomitantly acquired and interpreted full-dose contrast-enhanced CT seems to increase diagnostic error rate. These data can be used for quality improvement and benchmarking purposes.

Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician?

Radiomics, machine learning, and, more generally, artificial intelligence (AI) provide unique tools to improve the performances of nuclear medicine in all aspects. They may help rationalise the operational organisation of imaging departments, optimise resource allocations, and improve image quality while decreasing radiation exposure and maintaining qualitative accuracy. There is already convincing data that show AI detection, and interpretation algorithms can perform with equal or higher diagnostic accuracy in various specific indications than experts in the field. Preliminary data strongly suggest that AI will be able to process imaging data and information well beyond what is visible to the human eye, and it will be able to integrate features to provide signatures that may further drive personalised medicine. As exciting as these prospects are, they currently remain essentially projects with a long way to go before full validation and routine clinical implementation. AI uses a language that is totally unfamiliar to nuclear medicine physicians, who have not been trained to manage the highly complex concepts that rely primarily on mathematics, computer sciences, and engineering. Nuclear medicine physicians are mostly familiar with biology, pharmacology, and physics, yet, considering the disruptive nature of AI in medicine, we need to start acquiring the knowledge that will keep us in the position of being actors and not merely witnesses of the wonders developed by other stakeholders in front of our incredulous eyes. This will allow us to remain a useful and valid interface between the image, the data, and the patients and free us to pursue other, one might say nobler tasks, such as treating, caring and communicating with our patients or conducting research and development.

18F-FLT PET imaging of cellular proliferation in pancreatic cancer

Pancreatic ductal adenocarcinoma is known for its poor prognosis. Since the development of computerized tomography, magnetic resonance and endoscopic ultrasound, novel imaging techniques have struggled to get established in the management of patients diagnosed with pancreatic adenocarcinoma for several reasons. Thus, imaging assessment of pancreatic cancer remains a field with scope for further improvement. In contrast to cross-sectional anatomical imaging methods, molecular imaging modalities such as positron emission tomography (PET) can provide information on tumour function. Particularly, tumour proliferation may be assessed by measurement of intracellular thymidine kinase 1 (TK1) activity level using thymidine analogues radiolabelled with a positron emitter for use with PET. This approach, has been widely explored with [(18)F]-fluoro-3'-deoxy-3'-L-fluorothymidine ((18)F-FLT) PET. This manuscript reviews the rationale and physiology behind (18)F-FLT PET imaging, with special focus on pancreatic cancer and other gastrointestinal malignancies. Potential benefit and challenges of this imaging technique for diagnosis, staging and assessment of treatment response in abdominal malignancies are discussed.

Value of FDG PET/CT in Patient Management and Outcome of Skeletal and Soft Tissue Sarcomas

Fluorodeoxyglucose (FDG)-PET/computed tomography (CT) has been increasingly used in bone and soft tissue sarcomas and provides advantages in the initial tumor staging, tumor grading, therapy assessment, and recurrence detection. FDG-PET/CT metabolic parameters are reliable predictors of survival in sarcomas and could be implemented in risk stratification models along with other prognostic factors in these patients.

FDG PET/CT in Pancreatic and Hepatobiliary Carcinomas: Value to Patient Management and Patient Outcomes

Fludeoxyglucose F 18 ((18)F-FDG) PET/CT has not been shown to offer additional benefit in the initial diagnosis of pancreatic cancer, but studies show benefit of (18)F-FDG PET/CT in initial staging and patient prognosis. There is evidence for (18)F-FDG PET and (18)F-FDG PET/CT in staging and prognosis of cholangiocarcinoma and gallbladder cancer. (18)F-FDG PET/CT has shown promise in staging liver malignancies by detecting extrahepatic metastasis. There is evidence supporting the ability of PET/CT in predicting prognosis in patients with hepatocellular carcinoma. Evidence is evolving for the role of (18)F-FDGPET/CT in predicting prognosis and survival in patients with colorectal liver metastasis.