Comparison of 18F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy

Scott, James A.; McDermott, Shaunagh; Kilcoyne, Aoife; Wang, Y.; Halpern, Elkan F.; Ackman, Jeanne B.

doi:10.1016/j.crad.2019.04.024

Cited by 7 publications

(36 citation statements)

References 15 publications

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“…A total of 65 studies were available to be reviewed against the inclusion criteria. The included studies were those that developed an AI model based on 18 F-FDG PET data in cancer to diagnose, differentiate, delineate, stage, assess response to therapy, determine prognosis, or improve image quality. Figure 1 shows the flowchart of study selection process.…”

Section: Methodsmentioning

confidence: 99%

“…The most widely used PET-radiotracer in clinical practice is 2-deoxy-2-[ 18 F]fluoro-D-glucose ( 18 F-FDG), which provides an estimation of glucose metabolism in tissues (2). 18 F-FDG is the workhorse of oncological PET imaging and is recommended for staging, restaging, therapy response assessment, and recurrence detection of different cancers. In addition, PET imaging allows quantitative assessment of tumor glucose metabolism and provides additional diagnostic and prognostic information which is particularly helpful in monitoring therapy response assessment (3).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, PET imaging allows quantitative assessment of tumor glucose metabolism and provides additional diagnostic and prognostic information which is particularly helpful in monitoring therapy response assessment (3). Different 18 F-FDG PET-derived imaging biomarkers have been proposed for this purpose including tumor glycolytic activity as reflected by the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) (4).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it can help to optimize dose estimation, and provide automated detection of abnormalities, compare with prior studies and assess response to therapy, interpret and generate report, and correlate image findings with other clinical data (7). 18 F-FDG PET as a diagnostic imaging modality can benefit from AI in all the abovementioned aspects. The result will make the process faster and more accurate.…”

Section: Introductionmentioning

confidence: 99%

“…In this systematic review, we will summarize the relevant literature regarding the application of AI in 18 F-FDG PET/ CT Imaging of different cancers.…”

Section: Introductionmentioning

confidence: 99%

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Applications of artificial intelligence in oncologic 18F-FDG PET/CT imaging: a systematic review

Sadaghiani¹,

Rowe²,

Sheikhbahaei³

2021

Ann Transl Med

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Artificial intelligence (AI) is a growing field of research that is emerging as a promising adjunct to assist physicians in detection and management of patients with cancer. 18 F-FDG PET imaging helps physicians in detection and management of patients with cancer. In this study we discuss the possible applications of AI in 18 F-FDG PET imaging based on the published studies. A systematic literature review was performed in PubMed on early August 2020 to find the relevant studies. A total of 65 studies were available for review against the inclusion criteria which included studies that developed an AI model based on 18F-FDG PET data in cancer to diagnose, differentiate, delineate, stage, assess response to therapy, determine prognosis, or improve image quality. Thirty-two studies met the inclusion criteria and are discussed in this review. The majority of studies are related to lung cancer. Other studied cancers included breast cancer, cervical cancer, head and neck cancer, lymphoma, pancreatic cancer, and sarcoma.All studies were based on human patients except for one which was performed on rats. According to the included studies, machine learning (ML) models can help in detection, differentiation from benign lesions, segmentation, staging, response assessment, and prognosis determination. Despite the potential benefits of AI in cancer imaging and management, the routine implementation of AI-based models and 18 F-FDG PETderived radiomics in clinical practice is limited at least partially due to lack of standardized, reproducible, generalizable, and precise techniques.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this systematic review, we will summarize the relevant literature regarding the application of AI in 18 F-FDG PET/ CT Imaging of different cancers.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Applications of artificial intelligence in oncologic 18F-FDG PET/CT imaging: a systematic review

Sadaghiani¹,

Rowe²,

Sheikhbahaei³

2021

Ann Transl Med

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Artificial Intelligence for the Characterization of Pulmonary Nodules, Lung Tumors and Mediastinal Nodes on PET/CT

Krarup

Krokos

Subesinghe

et al. 2021

Seminars in Nuclear Medicine

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Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

Niu

Shao

et al. 2021

Quant Imaging Med Surg

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Background: To develop and verify a prediction model for distinguishing malignant from benign groundglass nodules (GGNs) combined with clinical characteristics and 18 F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) parameters.Methods: We retrospectively analyzed 170 patients (56 males and 114 females) with GGNs who underwent PET/CT and high-resolution CT examination in our hospital from November 2011 to December 2019.The clinical and imaging data of all patients were collected, and the nodules were randomly divided into a derivation set and a validation set. For the derivation set, we used multivariate logistic regression to develop a prediction model for distinguishing benign from malignant GGNs. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the model, and the data in the validation set were used to verify the prediction model.Results: Among the 170 patients, 197 GGNs were confirmed via postoperative pathological examination or clinical follow-up. There were 21 patients with 27 GGNs in the benign group and 149 patients with 170 GGNs in the adenocarcinoma group. A total of five parameters, including the patient's sex, nodule location, margin, pleural indentation, and standardized uptake value (SUV) index (the ratio of nodule SUVmax to liver SUVmean), were selected to develop a prediction model for distinguishing benign from malignant GGNs.The area under the curve (AUC) of the model was 0.875 in the derivation set, with a sensitivity of 0.702 and a specificity of 0.923. The positive likelihood ratio was 9.131, and the negative likelihood ratio was 0.322. In the validation set, the AUC of the model was 0.874, which was not significantly different from the derivation set (P=0.989).Conclusions: This study developed and validated a prediction model based on 18 F-FDG PET/CT imaging and clinical characteristics for distinguishing malignant from benign GGNs. The model showed good diagnostic efficacy and high specificity, which can improve the preoperative diagnosis of high-risk GGNs.

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Comparison of 18F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy

Cited by 7 publications

References 15 publications

Applications of artificial intelligence in oncologic 18F-FDG PET/CT imaging: a systematic review

Applications of artificial intelligence in oncologic 18F-FDG PET/CT imaging: a systematic review

Artificial Intelligence for the Characterization of Pulmonary Nodules, Lung Tumors and Mediastinal Nodes on PET/CT

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

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