A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study

The value of 18F-fluorodeoxyglucose positron emission tomography-based radiomics in non-small cell lung cancer

Currently, the second most commonly diagnosed cancer in the world is lung cancer, and 85% of cases are non-small cell lung cancer (NSCLC). With growing knowledge of oncogene drivers and cancer immunology, several novel therapeutics have emerged to improve the prognostic outcomes of NSCLC. However, treatment outcomes remain diverse, and an accurate tool to achieve precision medicine is an unmet need. Radiomics, a method of extracting medical imaging features, is promising for precision medicine. Among all radiomic tools, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)-based radiomics provides distinct information on glycolytic activity and heterogeneity. In this review, we collected relevant literature from PubMed and summarized the various applications of 18F-FDG PET-derived radiomics in improving the detection of metastasis, subtyping histopathologies, characterizing driver mutations, assessing treatment response, and evaluating survival outcomes of NSCLC. Furthermore, we reviewed the values of 18F-FDG PET-based deep learning. Finally, several challenges and caveats exist in the implementation of 18F-FDG PET-based radiomics for NSCLC. Implementing 18F-FDG PET-based radiomics in clinical practice is necessary to ensure reproducibility. Moreover, basic studies elucidating the underlying biological significance of 18F-FDG PET-based radiomics are lacking. Current inadequacies hamper immediate clinical adoption; however, radiomic studies are progressively addressing these issues. 18F-FDG PET-based radiomics remains an invaluable and indispensable aspect of precision medicine for NSCLC.

Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study

OBJECTIVE

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment.

METHODS

We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively.

RESULTS

In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively).

CONCLUSIONS

Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography

OBJECTIVES

A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant metastases in patients with locally advanced uterine cervical cancer.

METHODS

This study used baseline [18F]FDG-PET/CT images of newly diagnosed uterine cervical cancer patients. Data from 186 to 25 patients were analyzed for training and validation cohort, respectively. All patients received chemoradiotherapy (CRT) and follow-up. PET and CT images were augmented by using three-dimensional techniques. The proposed model employed DL to predict distant metastases. Receiver operating characteristic (ROC) curve analysis was performed to measure the model's predictive performance.

RESULTS

The area under the ROC curves of the training and validation cohorts were 0.818 and 0.830 for predicting distant metastasis, respectively. In the training cohort, the sensitivity, specificity, and accuracy were 80.0%, 78.0%, and 78.5%, whereas, the sensitivity, specificity, and accuracy for distant failure were 73.3%, 75.5%, and 75.2% in the validation cohort, respectively.

CONCLUSION

Through the use of baseline [ 18 F]FDG-PET/CT images, the proposed DL model can predict the development of distant metastases for patients with locally advanced uterine cervical cancer treatment by CRT. External validation must be conducted to determine the model's predictive performance.