Dataset Information

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

ABSTRACT: [¹⁸F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

SUBMITTER: Salehjahromi M

PROVIDER: S-EPMC10983039 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

Salehjahromi Morteza M Karpinets Tatiana V TV Sujit Sheeba J SJ Qayati Mohamed M Chen Pingjun P Aminu Muhammad M Saad Maliazurina B MB Bandyopadhyay Rukhmini R Hong Lingzhi L Sheshadri Ajay A Lin Julie J Antonoff Mara B MB Sepesi Boris B Ostrin Edwin J EJ Toumazis Iakovos I Huang Peng P Cheng Chao C Cascone Tina T Vokes Natalie I NI Behrens Carmen C Siewerdsen Jeffrey H JH Hazle John D JD Chang Joe Y JY Zhang Jianhua J Lu Yang Y Godoy Myrna C B MCB Chung Caroline C Jaffray David D Wistuba Ignacio I Lee J Jack JJ Vaporciyan Ara A AA Gibbons Don L DL Gladish Gregory G Heymach John V JV Wu Carol C CC Zhang Jianjun J Wu Jia J

Cell reports. Medicine 20240311 3

[<sup>18</sup>F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). S ...[more]

PMID: 38471502

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Project description:ObjectivesTo investigate, in patients with metastatic prostate cancer, whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone using 68 Ga-PSMA PET imaging as reference standard.MethodsIn this IRB-approved retrospective study, 67 patients (mean age 71 ± 7 years; range: 55-84 years) showing a total of 205 68 Ga-PSMA-positive prostate cancer bone metastases in the thoraco-lumbar spine and pelvic bone being invisible in CT were included. Metastases and 86 68 Ga-PSMA-negative bone volumes in the same body region were segmented and further post-processed. Intra- and inter-reader reproducibility was assessed, with ICCs < 0.90 being considered non-reproducible. To account for imbalances in the dataset, data augmentation was performed to achieve improved class balance and to avoid model overfitting. The dataset was split into training, test, and validation set. After a multi-step dimension reduction process and feature selection process, the 11 most important and independent features were selected for statistical analyses.ResultsA gradient-boosted tree was trained on the selected 11 radiomic features in order to classify patients' bones into bone metastasis and normal bone using the training dataset. This trained model achieved a classification accuracy of 0.85 (95% confidence interval [CI]: 0.76-0.92, p < .001) with 78% sensitivity and 93% specificity. The tuned model was applied on the original, non-augmented dataset resulting in a classification accuracy of 0.90 (95% CI: 0.82-0.98) with 91% sensitivity and 88% specificity.ConclusionOur proof-of-concept study indicates that radiomics may accurately differentiate unaffected bone from metastatic bone, being invisible by the human eye on CT.Key points• This proof-of-concept study showed that radiomics applied on CT images may accurately differentiate between bone metastases and metastatic-free bone in patients with prostate cancer. • Future promising applications include automatic bone segmentation, followed by a radiomics classifier, allowing for a screening-like approach in the detection of bone metastases.

Project description:This study assessed whether preoperative maximum standardized uptake value (SUVmax) of metastatic lymph nodes (LNs) measured by F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (F-FDG PET/CT) could improve the prediction of prognosis in gastric cancer.One hundred fifty-one patients with gastric cancer and pathologically confirmed LN involvement who had undergone preoperative F-FDG PET/CT prior to curative surgical resection were retrospectively enrolled. To obtain nodal SUVmax, a transaxial image representing the highest F-FDG uptake was carefully selected, and a region of interest was manually drawn on the highest F-FDG accumulating LN. Conventional prognostic parameters and PET findings (primary tumor and nodal SUVmax) were analyzed for prediction of recurrence-free survival (RFS) and overall survival (OS). Furthermore, prognostic accuracy of survival models was assessed using c-statistics.Of the 151 patients, 38 (25%) experienced recurrence and 34 (23%) died during follow-up (median follow-up, 48 months; range, 5-74 months). Twenty-seven patients (18%) showed positive F-FDG nodal uptake (range, 2.0-22.6). In these 27 patients, a receiver-operating characteristic curve demonstrated a nodal SUVmax of 2.8 to be the optimal cutoff for predicting RFS and OS. The univariate and multivariate analyses showed that nodal SUVmax (hazard ratio [HR]?= 2.71, P < 0.0001), pathologic N (pN) stage (HR = 2.58, P = 0.0058), and pathologic T (pT) stage (HR = 1.77, P = 0.0191) were independent prognostic factors for RFS. Also, nodal SUVmax (HR = 2.80, P < 0.0001) and pN stage (HR = 2.28, P = 0.0222) were independent prognostic factors for OS. A predictive survival model incorporating conventional risk factors (pT/pN stage) gave a c-statistic of 0.833 for RFS and 0.827 for OS, whereas a model combination of nodal SUVmax with pT/pN stage gave a c-statistic of 0.871 for RFS (P = 0.0355) and 0.877 for OS (P = 0.0313).Nodal SUVmax measured by preoperative F-FDG PET/CT is an independent prognostic factor for RFS and OS. Combining nodal SUVmax with pT/pN staging can improve survival prediction precision in patients with gastric cancer.

Dataset Information

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

Publications

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

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