Employing 18 F-FDG PET/CT for distinguishing benign from metastatic adrenal masses

A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer

Objective

The aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data.

Methods

A retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively.

Results

The standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly.

Conclusion

The feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.

18F-FDG-PET/CT Evaluation of Indeterminate Adrenal Masses in Noncancer Patients

CONTEXT

Adrenal tumors in noncancer patients are common.

OBJECTIVE

Evaluate performance of 18F-fluorodeoxyglucose positron emission tomography computed tomography (18F-FDG-PET/CT) in distinguishing between benign and malignant adrenal tumors.

DESIGN

Retrospective chart review 2010-2019.

SETTING

Academic institution.

PATIENTS

One hundred and seventeen noncancer patients, defined as having no history of cancer or with cancer in remission for ≥5 years, completed 18F-FDG-PET/CT to evaluate adrenal masses, with pathologic diagnoses or imaging follow-up (≥12 months).

INTERVENTION

18F-FDG-PET/CT of 117 indeterminate adrenal masses.

MAIN OUTCOME MEASURES

Receiver operator characteristic curve of the ratios of adrenal lesion standardized uptake value (SUV)max to liver SUVmean and of adrenal lesion SUVmax to aortic arch blood pool SUVmean were constructed.

RESULTS

Seventy benign and 47 malignant masses (35 adrenocortical carcinomas [ACCs], 12 adrenal metastases) were identified. Malignant masses had higher median liver SUV and blood pool SUV ratios than benign masses (6.2 and 7.4 vs 1.4 and 2.0, P < .001). Median liver and blood pool SUV ratios of ACC (6.1 and 7.3, respectively) and metastases (6.7 and 7.7, respectively) were higher than those of than adenomas (1.4 and 2.2, P < .05 for all comparisons). Optimal liver SUV ratio to discern between benign and malignant masses was 2.5, yielding 85% sensitivity, 90% specificity, and 7 false negative results (including 3 ACCs). Optimal blood pool SUV ratio was 3.4, yielding 83% sensitivity, 90% specificity, and 8 false negative results (including 4 ACCs).

CONCLUSION

When used in conjunction with other clinical assessments, 18F-FDG-PET/CT can be a valuable tool in evaluating adrenal masses in noncancer patients.