Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma

Park, Changhee; Na, Kwon Joong; Choi, Hongyoon; Ock, Chan‐Young; Ha, Seunggyun; Park, Samina; Keam, Bhumsuk; Kim, Tae Min; Paeng, Jin Chul; Park, In Kyu; Kang, Chang Hyun; Kim, Dong-Wan; Cheon, Gi Jeong; Kang, Keon Wook; Kim, Young Tae; Heo, Dae Seog

doi:10.7150/thno.50283

Cited by 48 publications

(39 citation statements)

References 40 publications

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“…To our knowledge, this is the first study to investigate the relationship between SUV max and TMB in patients with diverse cancers. Prior studies have investigated the complex relationship between tumor immune microenvironment and glucose metabolism rate and shown associations between metabolic and immune profiles [ 15 , 16 ]. 2-[ 18 F]FDG PET imaging has been suggested as a diagnostic tool to estimate tumor immune status [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

Relationship between tumor mutational burden and maximum standardized uptake value in 2-[18F]FDG PET (positron emission tomography) scan in cancer patients

et al. 2020

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Purpose Deriving links between imaging and genomic markers is an evolving field. 2-[18F]FDG PET/CT (18F-fluorodeoxyglucose positron emission tomography–computed tomography) is commonly used for cancer imaging, with maximum standardized uptake value (SUVmax) as the main quantitative parameter. Tumor mutational burden (TMB), the quantitative variable obtained using next-generation sequencing on a tissue biopsy sample, is a putative immunotherapy response predictor. We report the relationship between TMB and SUVmax, linking these two important parameters. Methods In this pilot study, we analyzed 1923 patients with diverse cancers and available TMB values. Overall, 273 patients met our eligibility criteria in that they had no systemic treatment prior to imaging/biopsy, and also had 2-[18F]FDG PET/CT within 6 months prior to the tissue biopsy, to ensure acceptable temporal correlation between imaging and genomic evaluation. Results We found a linear correlation between TMB and SUVmax (p < 0.001). In the multivariate analysis, only TMB independently correlated with SUVmax, whereas age, gender, and tumor organ did not. Conclusion Our observations link SUVmax in readily available, routinely used, and noninvasive 2-[18F]FDG PET/CT imaging to the TMB, which requires a tissue biopsy and time to process. Since higher TMB has been implicated as a prognostic biomarker for better outcomes after immunotherapy, further investigation will be needed to determine if SUVmax can stratify patient response to immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Relationship between tumor mutational burden and maximum standardized uptake value in 2-[18F]FDG PET (positron emission tomography) scan in cancer patients

et al. 2020

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show abstract

“…To our knowledge, this is the rst study to investigate the relationship between SUV max and TMB in patients with diverse cancers. Prior studies have investigated the complex relationship between tumor immune microenvironment and glucose metabolism rate and shown associations between metabolic and immune pro les [15,16]. 2-[ 18 F]FDG PET imaging has been suggested as a diagnostic tool to estimate tumor immune status [17].…”

Section: Discussionmentioning

confidence: 99%

Relationship between tumor mutational burden and maximum standardized uptake value in 2-[18F]FDG PET (positron emission tomography) scan in cancer patients

Jahromi

Barkauskas

Zabel

et al. 2020

Preprint

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Purpose: Deriving links between imaging and genomic markers is an evolving field. 2-[18F]FDG PET/CT (18F-fluorodeoxyglucose Positron Emission Tomography- Computed Tomography) is commonly used for cancer imaging, with maximum standardized uptake value (SUVmax) as the main quantitative parameter. Tumor mutational burden (TMB), the quantitative variable obtained using next-generation sequencing on a tissue biopsy sample, is a putative immunotherapy response predictor. We report the relationship between TMB and SUVmax, linking these two important parameters.Methods: In this pilot study, we analyzed 1923 patients with diverse cancers and available TMB values. Overall, 273 patients met our eligibility criteria in that they had no systemic treatment prior to imaging/biopsy, and also had 2-[18F]FDG PET/CT within six months prior to the tissue biopsy, to ensure acceptable temporal correlation between imaging and genomic evaluation.Results: We found a linear correlation between TMB and SUVmax (p<0.001). In the multivariate analysis, only TMB independently correlated with SUVmax whereas age, gender and tumor organ did not.Conclusion: Our observations link SUVmax in readily available, routinely used, and non-invasive 2-[18F]FDG PET/CT imaging to the TMB, which requires a tissue biopsy and time to process. Since higher TMB has been implicated as a prognostic biomarker for better outcomes after immunotherapy, further investigation will be needed to determine if SUVmax can stratify patient response to immunotherapy.

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“…Some studies indicated that both PET-based radiomic features provided the prediction of prognostic outcome for lung cancer patients with radiotherapy or chemo-radiotherapy, whereas semiquantitative PET factors were not available [96]. Both PET-based handcrafted and deep features can predict the response of immunotherapy in lung adenocarcinoma [97,98].…”

Section: Applications Of Functional Radiomic Features In Lung Cancermentioning

confidence: 99%

Structural and functional radiomics for lung cancer

Jochems

Ibrahim

et al. 2021

Eur J Nucl Med Mol Imaging

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Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

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Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma

Cited by 48 publications

References 40 publications

Relationship between tumor mutational burden and maximum standardized uptake value in 2-[18F]FDG PET (positron emission tomography) scan in cancer patients

Relationship between tumor mutational burden and maximum standardized uptake value in 2-[18F]FDG PET (positron emission tomography) scan in cancer patients

Relationship between tumor mutational burden and maximum standardized uptake value in 2-[18F]FDG PET (positron emission tomography) scan in cancer patients

Structural and functional radiomics for lung cancer

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