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Lee JW, Ahn H, Yoo ID, Hong SP, Baek MJ, Kang DH, Lee SM. Relationship of FDG PET/CT imaging features with tumor immune microenvironment and prognosis in colorectal cancer: a retrospective study. Cancer Imaging 2024; 24:53. [PMID: 38627864 PMCID: PMC11020988 DOI: 10.1186/s40644-024-00698-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/09/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Imaging features of colorectal cancers on 2-deoxy-2-[18F]fluoro-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) have been considered to be affected by tumor characteristics and tumor immune microenvironment. However, the relationship between PET/CT imaging features and immune reactions in tumor tissue has not yet been fully evaluated. This study investigated the association of FDG PET/CT imaging features in the tumor, bone marrow, and spleen with immunohistochemical results of cancer tissue and recurrence-free survival (RFS) in patients with colorectal cancer. METHODS A total of 119 patients with colorectal cancer who underwent FDG PET/CT for staging work-up and received curative surgical resection were retrospectively enrolled. From PET/CT images, 10 first-order imaging features of primary tumors, including intensity of FDG uptake, volumetric metabolic parameters, and metabolic heterogeneity parameters, as well as FDG uptake in the bone marrow and spleen were measured. The degrees of CD4+, CD8+, and CD163 + cell infiltration and interleukin-6 (IL-6) and matrix metalloproteinase-11 (MMP-11) expression were graded through immunohistochemical analysis of surgical specimens. The relationship between FDG PET/CT imaging features and immunohistochemical results was assessed, and prognostic significance of PET/CT imaging features in predicting RFS was evaluated. RESULTS Correlation analysis with immunohistochemistry findings showed that the degrees of CD4 + and CD163 + cell infiltration and IL-6 and MMP-11 expression were correlated with cancer imaging features on PET/CT. Patients with enhanced inflammatory response in cancer tissue demonstrated increased FDG uptake, volumetric metabolic parameters, and metabolic heterogeneity. FDG uptake in the bone marrow and spleen was positively correlated with the degree of CD163 + cell infiltration and IL-6 expression, respectively. In multivariate survival analysis, the coefficient of variation of FDG uptake in the tumor (p = 0.019; hazard ratio, 0.484 for 0.10 increase) and spleen-to-liver uptake ratio (p = 0.020; hazard ratio, 24.901 for 1.0 increase) were significant independent predictors of RFS. CONCLUSIONS The metabolic heterogeneity of tumors and FDG uptake in the spleen were correlated with tumor immune microenvironment and showed prognostic significance in predicting RFS in patients with colorectal cancer.
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Affiliation(s)
- Jeong Won Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam- gu, 31151, Cheonan, Korea
| | - Hyein Ahn
- Department of Pathology, CHA Gangnam Medical Center, CHA University School of Medicine, 569 Nonhyon-ro, Gangnam-gu, 06135, Seoul, Korea
| | - Ik Dong Yoo
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam- gu, 31151, Cheonan, Korea
| | - Sun-Pyo Hong
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam- gu, 31151, Cheonan, Korea
| | - Moo-Jun Baek
- Department of Surgery, College of Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6- gil, Dongnam-gu, 31151, Cheonan, Korea
| | - Dong Hyun Kang
- Department of Colorectal surgery, College of Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, 31151, Cheonan, Korea
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam- gu, 31151, Cheonan, Korea.
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Lin Y, Yang Z, Chen J, Li M, Cai Z, Wang X, Zhai T, Lin Z. A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients. Eur Radiol 2024; 34:1302-1313. [PMID: 37594526 DOI: 10.1007/s00330-023-09987-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 08/19/2023]
Abstract
OBJECTIVES To develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy. METHODS LA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m2), were randomly divided into training and validation groups. 107 radiomics features based on the primary nasopharyngeal tumor were extracted from each pre-treatment CECT scan. Through Cox regression analysis, a radiomics model and patients' corresponding radiomics scores were created with predictive independent radiomics features. T stage (T) and radiomics score (R) were compared as predictive factors. Combining the N stage (N), a clinical model (T + N), and a substitution model (R + N) were constructed. RESULTS Training and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018). CONCLUSION Our radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy. CLINICAL RELEVANCE STATEMENT Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies. KEY POINTS • A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.
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Affiliation(s)
- Yinbing Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Zhining Yang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Jiechen Chen
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Mei Li
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Zeman Cai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Xiao Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Tiantian Zhai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China.
| | - Zhixiong Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China.
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Philip MM, Welch A, McKiddie F, Nath M. A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients. Cancer Med 2023; 12:16181-16194. [PMID: 37353996 PMCID: PMC10469753 DOI: 10.1002/cam4.6278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/07/2023] [Accepted: 06/11/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Positron emission tomography (PET) images of head and neck squamous cell carcinoma (HNSCC) patients can assess the functional and biochemical processes at cellular levels. Therefore, PET radiomics-based prediction and prognostic models have the potentials to understand tumour heterogeneity and assist clinicians with diagnosis, prognosis and management of the disease. We conducted a systematic review of published modelling information to evaluate the usefulness of PET radiomics in the prediction and prognosis of HNSCC patients. METHODS We searched bibliographic databases (MEDLINE, Embase, Web of Science) from 2010 to 2021 and considered 31 studies with pre-defined inclusion criteria. We followed the CHARMS checklist for data extraction and performed quality assessment using the PROBAST tool. We conducted a meta-analysis to estimate the accuracy of the prediction and prognostic models using the diagnostic odds ratio (DOR) and average C-statistic, respectively. RESULTS Manual segmentation method followed by 40% of the maximum standardised uptake value (SUVmax ) thresholding is a commonly used approach. The area under the receiver operating curves of externally validated prediction models ranged between 0.60-0.87, 0.65-0.86 and 0.62-0.75 for overall survival, distant metastasis and recurrence, respectively. Most studies highlighted an overall high risk of bias (outcome definition, statistical methodologies and external validation of models) and high unclear concern in terms of applicability. The meta-analysis showed the estimated pooled DOR of 6.75 (95% CI: 4.45, 10.23) for prediction models and the C-statistic of 0.71 (95% CI: 0.67, 0.74) for prognostic models. CONCLUSIONS Both prediction and prognostic models using clinical variables and PET radiomics demonstrated reliable accuracy for detecting adverse outcomes in HNSCC, suggesting the prospect of PET radiomics in clinical settings for diagnosis, prognosis and management of HNSCC patients. Future studies of prediction and prognostic models should emphasise the quality of reporting, external model validation, generalisability to real clinical scenarios and enhanced reproducibility of results.
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Affiliation(s)
| | - Andy Welch
- Institute of Education in Healthcare and Medical Sciences, University of AberdeenAberdeenUK
| | | | - Mintu Nath
- Institute of Applied Health Sciences, University of AberdeenAberdeenUK
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Elahmadawy MA, Ashraf A, Moustafa H, Kotb M, Abd El-Gaid S. Prognostic value of initial [ 18 F]FDG PET/computed tomography volumetric and texture analysis-based parameters in patients with head and neck squamous cell carcinoma. Nucl Med Commun 2023; 44:653-662. [PMID: 37038954 DOI: 10.1097/mnm.0000000000001695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
AIM OF WORK To determine the predictive value of initial [ 18 F]FDG PET/computed tomography (CT) volumetric and radiomics-derived analyses in patients with head and neck squamous cell carcinoma (HNSCC). METHODS Forty-six adult patients had pathologically proven HNSCC and underwent pretherapy [ 18 F]FDG PET/CT were enrolled. Semi-quantitative PET-derived volumetric [(maximum standardized uptake value (SUVmax) and mean SUV (SUVmean), total lesion glycolysis (TLG) and metabolic tumor volume (MTV)] and radiomics analyses using LIFEx 6.73.3 software were performed. RESULTS In the current study group, the receiver operating characteristic curve marked a cutoff point of 21.105 for primary MTV with area under the curve (AUC) of 0.727, sensitivity of 62.5%, and specificity of 86.8% ( P value 0.041) to distinguish responders from non-responders, while no statistically significant primary SUVmean or max or primary TLG cut off points could be determined. It also marked the cutoff point for survival prediction of 10.845 for primary MTV with AUC 0.728, sensitivity of 80%, and specificity of 77.8% ( P value 0.026). A test of the synergistic performance of PET-derived volumetric and textural features significant parameters was conducted in an attempt to develop the most accurate and stable prediction model. Therefore, multivariate logistic regression analysis was performed to detect independent predictors of mortality. With a high specificity of 97.1% and an overall accuracy of 89.1%, the combination of primary tumor MTV and the textural feature gray-level co-occurrence matrix correlation provided the most accurate prediction of mortality ( P value < 0.001). CONCLUSION Textural feature indices are a noninvasive method for capturing intra-tumoral heterogeneity. In our study, a PET-derived prediction model was successfully generated with high specificity and accuracy.
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Affiliation(s)
| | - Aya Ashraf
- Nuclear Medicine Unit, National Cancer Institute
| | - Hosna Moustafa
- Nuclear Medicine Unit, Kasr Al-Ainy (NEMROCK Center), Cairo University, Cairo, Egypt
| | - Magdy Kotb
- Nuclear Medicine Unit, National Cancer Institute
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Kim SJ, Choi JY, Ahn YC, Ahn MJ, Moon SH. The prognostic value of radiomic features from pre- and post-treatment 18F-FDG PET imaging in patients with nasopharyngeal carcinoma. Sci Rep 2023; 13:8462. [PMID: 37231092 DOI: 10.1038/s41598-023-35582-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 05/20/2023] [Indexed: 05/27/2023] Open
Abstract
Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose (FDG) is widely used for management of nasopharyngeal carcinoma (NPC). Combining the radiomic features of pre- and post-treatment FDG PET images may improve tumor characterization and prognostic predication. We investigated prognostic value of radiomic features from pre- and post-radiotherapy FDG PET images in patients with NPC. Quantitative radiomic features of primary tumors were extracted from the FDG PET images of 145 NPC patients and the delta values were also calculated. The study population was divided randomly into two groups, the training and test sets (7:3). A random survival forest (RSF) model was adopted to perform analyses of progression-free survival (PFS) and overall survival (OS). There were 37 (25.5%) cases of recurrence and 16 (11.0%) cases of death during a median follow-up period of 54.5 months. Both RSF models with clinical variables and radiomic PET features for PFS and OS showed comparable predictive performance to RSF models with clinical variables and conventional PET parameters. Tumoral radiomic features of pre- and post-treatment FDG PET and the corresponding delta values may predict PFS and OS in patients with NPC.
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Affiliation(s)
- Soo Jeong Kim
- Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Yong Chan Ahn
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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Prognostic value of textural features obtained from F-fluorodeoxyglucose (F-18 FDG) positron emission tomography/computed tomography (PET/CT) in patients with locally advanced cervical cancer undergoing concurrent chemoradiotherapy. Ann Nucl Med 2023; 37:44-51. [PMID: 36369325 DOI: 10.1007/s12149-022-01802-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 10/23/2022] [Indexed: 11/13/2022]
Abstract
OBJECTIVE To evaluate whether textural features obtained from F-18 FDG PET/CT offer clinical value that can predict the outcome of patients with locally advanced cervical cancer (LACC) receiving concurrent chemoradiotherapy (CCRT). METHODS We reviewed the records of 68 patients with stage IIB-IVA LACC who underwent PET/CT before CCRT. Conventional metabolic parameters, shape indices, and textural features of the primary tumor were measured on PET/CT. A Cox regression model was used to examine the effects of variables on overall survival (OS) and progression-free survival (PFS). RESULTS The patients included in this study were classified into two groups based on median value of PET/CT parameters. The high group of GLNU derived from GLRLM is only independent prognostic factor for PFS (HR 7.142; 95% CI 1.656-30.802; p = 0.008) and OS (HR 9,780; 95% CI 1.222-78.286; p = 0.031). In addition, GLNU derived from GLRLM (AUC 0.846, 95% CI 0.738-0.923) was the best predictor for recurrence among clinical prognostic factors and PET/CT parameters. CONCLUSION Our results demonstrated that high GLNU from GLRLM on pretreatment F-18 FDG PET/CT images, were significant prognostic factors for recurrence and death in patients with LACC receiving CCRT.
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Jin A, Lin X, Yin X, Cui Y, Ma L. Prognostic value of MTV and TLG of 18 F-FDG PET in patients with head and neck squamous cell carcinoma: A meta-analysis. Medicine (Baltimore) 2022; 101:e30798. [PMID: 36181127 PMCID: PMC9524907 DOI: 10.1097/md.0000000000030798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The current systematic review and meta-analysis explored the value of metabolic tumor volume (MTV) as well as total lesion glycolysis (TLG) in predicting the prognosis of head and neck squamous cell carcinoma (HNSCC) using 18 F-FDG PET parameters. METHODS This work identified relevant studies in the English language by searching several electronic databases, like Cochrane Library, EMBASE, and PubMed. In addition, pooled hazard ratios (HRs) were also calculated to analyze whether MTV and TLG were significant in predicting prognosis. RESULTS The present study included 15 primary studies involving HNSCC cases. As for the elevated TLG, it attained the pooled HR of 1.85 (95% confidence interval [CI], 1.16-2.94; P = .000; I2 = 78.3%) in predicting overall survival (OS), whereas that for elevated MTV was1.22 (95%CI, 1.09-1.36; P = .000; I2 = 82.4%). Besides, for elevated MTV, it attained the pooled HR of 1.34 (95%CI, 1.15-1.56, P = .000; I2 = 86.0%) in predicting disease-free survival (DFS); while the elevated TLG was related to DFS. Sensitivity analysis confirmed that our results are reliable. As for MTV, the ROC-stratified subgroups for DFS and multivariate analyses-stratified subgroups for OS showed statistically significant differences, with no obvious heterogeneities across different studies. For TLG, other methods-stratified subgroups for OS showed statistically significant differences, with no obvious heterogeneity across different studies. CONCLUSION This work indicated that PET/CT is of predictive significance across HNSCC cases. Although the included articles used different methods and recruited HNSCC cases with high clinical heterogeneity; however, our findings confirmed that an elevated MTV can predict the increased risk of side reactions or even death among HNSCC cases and that an elevated TLG can predict a higher death risk.
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Affiliation(s)
- Aihua Jin
- Department of Clinical Laboratory, Yanbian University Hospital, Yanji, Jilin Province, China
| | - Xing Lin
- Department of Thoracic Surgery, Yanbian University Hospital, Yanji, Jilin Province, China
| | - Xuezhe Yin
- Department of Respiration Medicine, Yanbian University Hospital, Yanji, Jilin Province, China
| | - Yinfeng Cui
- Department of Stomatology, Medical College of Yanbian University, Jilin Province, China
- *Correspondence: Liguang Ma and Yinfeng Cui, Department of College of Yanbian University, Jilin Province 133000, China (e-mail: and )
| | - Liguang Ma
- Department of Stomatology, Medical College of Yanbian University, Jilin Province, China
- *Correspondence: Liguang Ma and Yinfeng Cui, Department of College of Yanbian University, Jilin Province 133000, China (e-mail: and )
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An Investigation on Radiomics Feature Handling for HNSCC Staging Classification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The incidence of Head and Neck Squamous Cell Carcinoma (HNSCC) has been growing in the last few decades. Its diagnosis is usually performed through clinical evaluation and analyzing radiological images, then confirmed by histopathological examination, an invasive and time-consuming operation. The recent advances in the artificial intelligence field are leading to interesting results in the early diagnosis, personalized treatment and monitoring of HNSCC only by analyzing radiological images, without performing a tissue biopsy. The large amount of radiological images and the increasing interest in radiomics approaches can help to develop machine learning (ML) methods to support diagnosis. In this work, we propose an ML method based on the use of radiomics features, extracted from CT and PET images, to classify the disease in terms of pN-Stage, pT-Stage and Overall Stage. After the extraction of radiomics features, a selection step is performed to remove dataset redundancy. Finally, ML methods are employed to complete the classification task. Our pipeline is applied on the “Head-Neck-PET-CT” TCIA open-source dataset, considering a cohort of 201 patients from four different institutions. An AUC of 97%, 83% and 93% in terms of pN-Stage, pT-Stage and Overall Stage classification, respectively, is achieved. The obtained results are promising, showing the potential efficiency of the use of radiomics approaches in staging classification.
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Application Values of 2D and 3D Radiomics Models Based on CT Plain Scan in Differentiating Benign from Malignant Ovarian Tumors. BIOMED RESEARCH INTERNATIONAL 2022; 2022:5952296. [PMID: 35224097 PMCID: PMC8872698 DOI: 10.1155/2022/5952296] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/25/2022] [Indexed: 12/23/2022]
Abstract
Background Accurate identification of ovarian tumors as benign or malignant is highly crucial. Radiomics is a new branch of imaging that has emerged in recent years to replace the traditional naked eye qualitative diagnosis. Objective This study is aimed at exploring the difference in the application potential of two- (2D) and three-dimensional (3D) radiomics models based on CT plain scan in differentiating benign from malignant ovarian tumors. Method A retrospective analysis was performed on 140 patients with ovarian tumors confirmed by surgery and pathology in our hospital from July 2017 to August 2020. These 140 patients were divided into benign group and malignant group according to the pathological results. The ITK-SNAP software was used to outline the regions-of-interest (ROI) of 2D or 3D tumors on the CT plain scan image of each patient; the texture features were extracted through analysis kit (AK), and the cases were randomly divided into training groups (n = 99) and validation group (n = 41) in a ratio of 7 : 3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to perform dimensionality reduction, followed by the construction of the radiomics nomogram model using the logistic regression method. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve and decision curve analysis (DCA) were used to evaluate and verify the results of the radiomics nomogram and compare the differences between 2D and 3D diagnostic performance. Results There were 396 quantitative radiomics feature parameters extracted from 2D group and the 3D group, respectively. The area under the curve (AUC) of the radiomics nomogram of the 2D training group and the validation group were 0.96 and 0.97, respectively. The accuracy, specificity, and sensitivity of the training set were 92.9%, 88.9%, and 96.3%, respectively, and those of the validation set were 90.2%, 82.6%, and 100.0%, respectively. The AUCs of the radiomics nomogram of the 3D training group and validation group were 0.96% and 0.99%, respectively. The accuracy, sensitivity, and specificity of the training set were 92.9%, 96.3%, and 88.9%, respectively, and those of the validation set were 97.6%, 95.7%, and 100.0%, respectively. DeLong's test indicated that there was no statistical significance between the two sets (P > 0.05). Conclusions For the differential diagnosis of benign and malignant ovarian tumors, the 2D and 3D radiomics nomogram models exhibited comparable diagnostic performance. Considering that the 2D model was cost-effective and time-efficient, it was more recommended to use 2D features in future research.
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Hirata K, Sugimori H, Fujima N, Toyonaga T, Kudo K. Artificial intelligence for nuclear medicine in oncology. Ann Nucl Med 2022; 36:123-132. [PMID: 35028877 DOI: 10.1007/s12149-021-01693-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 11/07/2021] [Indexed: 12/12/2022]
Abstract
As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.
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Affiliation(s)
- Kenji Hirata
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan. .,Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan. .,Division of Medical AI Education and Research, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
| | | | - Noriyuki Fujima
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.,Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.,Division of Medical AI Education and Research, Hokkaido University Graduate School of Medicine, Sapporo, Japan.,Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan.,Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Sapporo, Japan
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Lee JW, Park SH, Ahn H, Lee SM, Jang SJ. Predicting Survival in Patients with Pancreatic Cancer by Integrating Bone Marrow FDG Uptake and Radiomic Features of Primary Tumor in PET/CT. Cancers (Basel) 2021; 13:cancers13143563. [PMID: 34298775 PMCID: PMC8304187 DOI: 10.3390/cancers13143563] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/07/2021] [Accepted: 07/14/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary FDG uptake of bone marrow (BM) is known to reflect the degree of host inflammatory response to cancer cells and showed significant association with survival in diverse kinds of cancers. The aim of this retrospective study was to evaluate the prognostic significance of FDG uptake of BM and to investigate whether integrating FDG uptake of BM and radiomic features of primary tumors could improve the prediction of overall survival (OS) in patients with pancreatic cancer. In multivariable survival analysis, along with total lesion glycolysis (TLG) and first-order entropy of primary tumor lesions, FDG uptake of BM was an independent predictor of OS. We designed a PET/CT scoring system based on the cumulative scores of tumor factors (TLG and first-order entropy) and host factors (FDG uptake of BM). This scoring system was able to stratify the patients with three distinct prognostic groups independent of clinical stage and treatment modality. Abstract The purpose of this study was to evaluate the prognostic significance of FDG uptake of bone marrow (BM SUV) and to investigate its role combined with radiomic features of primary tumors in improving the prediction of overall survival (OS) in patients with pancreatic cancer. We retrospectively enrolled 65 pancreatic cancer patients with staging FDG PET/CT. BM SUV and conventional imaging parameters of primary tumors including total lesion glycolysis (TLG) were measured. First-order and higher-order textural features of primary cancer were extracted using PET textural analysis. Associations of PET/CT parameters of bone marrow (BM) and primary cancer with OS were assessed. BM SUV as well as TLG and first-order entropy of pancreatic cancer were significant independent predictors of OS in multivariable analysis. A PET/CT scoring system based on the cumulative scores of these three independent predictors enabled patient stratification into three distinct prognostic groups. The scoring system yielded a good prognostic stratification based on subgroup analysis irrespective of tumor stage and treatment modality. BM SUV was an independent predictor of OS in pancreatic cancer patients. The PET/CT scoring system that integrated PET/CT parameters of primary tumors and BM can provide prognostic information in pancreatic cancer independent of tumor stage and treatment.
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Affiliation(s)
- Jeong Won Lee
- Department of Nuclear Medicine, Catholic Kwandong University College of Medicine, International St. Mary’s Hospital, Incheon 22711, Korea;
| | - Sang-Heum Park
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea;
| | - Hyein Ahn
- Department of Pathology, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea;
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea
- Correspondence: (S.M.L.); (S.J.J.); Tel.: +82-41-570-3540 (S.M.L.); +82-31-780-5687 (S.J.J.)
| | - Su Jin Jang
- Department of Nuclear Medicine, CHA Bundang Medical Center, CHA University, Seongnam-si 13496, Korea
- Correspondence: (S.M.L.); (S.J.J.); Tel.: +82-41-570-3540 (S.M.L.); +82-31-780-5687 (S.J.J.)
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