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Lyu Y, Chen X, Liu H, Xi Y, Feng W, Li B. Comparison of the diagnostic value of [ 68 Ga]Ga-FAPI-04 PET/MR and [ 18F]FDG PET/CT in patients with T stage ≤ 2a2 uterine cervical cancer: a prospective study. Eur J Nucl Med Mol Imaging 2024; 51:1163-1172. [PMID: 38032383 DOI: 10.1007/s00259-023-06539-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023]
Abstract
PURPOSE To compare the diagnostic value of [68 Ga]Ga-FAPI-04 PET/MR and [18F]FDG PET/CT in patients with T stage ≤ 2a2 uterine cervical cancer patients. METHODS Patients pathologically diagnosed with cervical cancer and with a T stage ≤ T2a2 were prospectively enrolled. All patients underwent whole-body [68 Ga]Ga-FAPI-04 PET/MR and [18F]FDG PET/CT within 2 weeks, and surgical treatment was performed within 10 days after PET. RESULTS Twenty-five patients were enrolled. Twenty patients underwent radical hysterectomy, among which all of them underwent pelvic lymphadenectomy, and 10 patients underwent para-aortic lymphadenectomy. Three patients received merely laparoscopic lymphadenectomy without hysterectomy. Two patients with both [18F]FDG and [68 Ga]Ga-FAPI-04 lymph node high metabolism were staged as FIGO IIIC1r, and concurrent chemoradiation therapy (CCRT) was performed. [18F]FDG and [68 Ga]Ga-FAPI-04 had equivalent detection ability on primary tumors, with a positive detection rate of 96.0%. The accuracy of T staging using [18F]FDG and [68 Ga]Ga-FAPI-04 was relatively 50% and 55.0%. Elevated and underrated staging was due to misdiagnosis of either vaginal infiltration or tumor size. In terms of lymph node metastasis detection, the specificity of [68 Ga]Ga-FAPI-04 was 100% (95% CI, 84.6% ~ 100.0%), which was significantly higher than [18F]FDG (59.1% (95% CI, 36.4% ~ 79.3%)) (p = 0.004). CONCLUSION [68 Ga]Ga-FAPI-04 PET/MR and [18F]FDG PET/CT demonstrated an equivalent detection ability on cervical cancer primary tumors. However, [68 Ga]Ga-FAPI-04 PET/MR's diagnostic value in lymph node metastasis was significantly higher than [18F]FDG PET/CT. [68 Ga]Ga-FAPI-04 PET/MR has the potential for more accurate treatment planning, thus clarifying fertility preservation indications for early-stage young patients.
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Affiliation(s)
- You Lyu
- Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Xiaoyue Chen
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
- Collaboration Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Hua Liu
- Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Yun Xi
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
- Collaboration Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Weiwei Feng
- Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China.
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China.
- Collaboration Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China.
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Zerunian M, Nacci I, Caruso D, Polici M, Masci B, De Santis D, Mercantini P, Arrivi G, Mazzuca F, Paolantonio P, Pilozzi E, Vecchione A, Tarallo M, Fiori E, Iannicelli E, Laghi A. Is CT Radiomics Superior to Morphological Evaluation for pN0 Characterization? A Pilot Study in Colon Cancer. Cancers (Basel) 2024; 16:660. [PMID: 38339411 PMCID: PMC10854865 DOI: 10.3390/cancers16030660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/02/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
The aim of this study was to compare CT radiomics and morphological features when assessing benign lymph nodes (LNs) in colon cancer (CC). This retrospective study included 100 CC patients (test cohort) who underwent a preoperative CT examination and were diagnosed as pN0 after surgery. Regional LNs were scored with a morphological Likert scale (NODE-SCORE) and divided into two groups: low likelihood (LLM: 0-2 points) and high likelihood (HLM: 3-7 points) of malignancy. The T-test and the Mann-Whitney test were used to compare 107 radiomic features extracted from the two groups. Radiomic features were also extracted from primary lesions (PLs), and the receiver operating characteristic (ROC) was used to test a LN/PL ratio when assessing the LN's status identified with radiomics and with the NODE-SCORE. An amount of 337 LNs were divided into 167 with LLM and 170 with HLM. Radiomics showed 15/107 features, with a significant difference (p < 0.02) between the two groups. The comparison of selected features between 81 PLs and the corresponding LNs showed all significant differences (p < 0.0001). According to the LN/PL ratio, the selected features recognized a higher number of LNs than the NODE-SCORE (p < 0.001). On validation of the cohort of 20 patients (10 pN0, 10 pN2), significant ROC curves were obtained for LN/PL busyness (AUC = 0.91; 0.69-0.99; 95% C.I.; and p < 0.001) and for LN/PL dependence entropy (AUC = 0.76; 0.52-0.92; 95% C.I.; and p = 0.03). The radiomics ratio between CC and LNs is more accurate for noninvasively discriminating benign LNs compared to CT morphological features.
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Affiliation(s)
- Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
- Ph.D. School in Translational Medicine and Oncology, Department of Medical and Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Via Giorgio Nicola Papanicolau–ang. Via di Grottarossa 1035, 00189 Rome, Italy
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
- Ph.D. School in Translational Medicine and Oncology, Department of Medical and Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Via Giorgio Nicola Papanicolau–ang. Via di Grottarossa 1035, 00189 Rome, Italy
| | - Benedetta Masci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Domenico De Santis
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Paolo Mercantini
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy;
| | - Giulia Arrivi
- Oncology Unit, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (G.A.); (F.M.)
| | - Federica Mazzuca
- Oncology Unit, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (G.A.); (F.M.)
| | - Pasquale Paolantonio
- Department of Radiology, San Giovanni Addolorata Hospital Complex, Via dell’Amba Aradam 8, 00184 Rome, Italy;
| | - Emanuela Pilozzi
- Pathology Unit, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (E.P.); (A.V.)
| | - Andrea Vecchione
- Pathology Unit, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (E.P.); (A.V.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, Via Giovanni Maria Lancisi 2, 00161 Rome, Italy; (M.T.); (E.F.)
| | - Enrico Fiori
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, Via Giovanni Maria Lancisi 2, 00161 Rome, Italy; (M.T.); (E.F.)
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
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Guglielmo P, Marturano F, Bettinelli A, Sepulcri M, Pasello G, Gregianin M, Paiusco M, Evangelista L. Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature. Diagnostics (Basel) 2023; 13:2153. [PMID: 37443547 DOI: 10.3390/diagnostics13132153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/05/2023] [Accepted: 06/17/2023] [Indexed: 07/15/2023] Open
Abstract
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([18F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [18F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.
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Affiliation(s)
- Priscilla Guglielmo
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Francesca Marturano
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Andrea Bettinelli
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Matteo Sepulcri
- Radiotherapy, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Giulia Pasello
- Department of Surgery, Oncology and Gastroenterology, University of Padua, 35128 Padua, Italy
- Medical Oncology 2, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Michele Gregianin
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Marta Paiusco
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine DIMED, University of Padua, 35128 Padua, Italy
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Genseke P, Wielenberg CF, Schreiber J, Luecke E, Frese S, Walles T, Kreissl MC. Prospective Evaluation of Quantitative F-18-FDG-PET/CT for Pre-Operative Thoracic Lymph Node Staging in Patients with Lung Cancer as a Target for Computer-Aided Diagnosis. Diagnostics (Basel) 2023; 13:diagnostics13071263. [PMID: 37046481 PMCID: PMC10093566 DOI: 10.3390/diagnostics13071263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/22/2023] [Accepted: 03/24/2023] [Indexed: 03/30/2023] Open
Abstract
Purpose: Pre-operative assessment of thoracic lymphonodal (LN) involvement in patients with lung cancer (LC) is crucial when choosing the treatment modality. Visual assessment of F-18-FDG-PET/CT (PET/CT) is well established, however, there is still a need for prospective quantitative data to differentiate benign from malignant lesions which would simplify staging and guide the further implementation of computer-aided diagnosis (CAD). Methods: In this prospective study, 37 patients with confirmed lung cancer (m/f = 24/13; age: 70 [52–83] years) were analyzed. All patients underwent PET/CT and quantitative data (standardized uptake values) were obtained. Histological results were available for 101 thoracic lymph nodes. Quantitative data were matched to determine cut-off values for delineation between benign vs. malignant lymph nodes. Furthermore, a scoring system derived from these cut-off values was established. Statistical analyses were performed through ROC analysis. Results: Quantitative analysis revealed the optimal cut-off values (p < 0.01) for the differentiation between benign and malignant thoracic lymph nodes in patients suffering from lung cancer. The respective areas under the curve (AUC) ranged from 0.86 to 0.94. The highest AUC for a ratio of lymph node to healthy lung tissue was 0.94. The resulting accuracy ranged from 78.2% to 89.1%. A dedicated scoring system led to an AUC of 0.93 with a negative predictive value of 95.4%. Conclusion: Quantitative analysis of F-18-FDG-PET/CT data provides reliable results for delineation between benign and malignant thoracic lymph nodes. Thus, quantitative parameters can improve diagnostic accuracy and reliability and can also facilitate the handling of the steadily increasing number of clinical examinations.
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AlRasheedi M, Han S, Thygesen H, Neilson M, Hendry F, Alkarn A, Maclay JD, Leung HY. A Comparative Evaluation of Mediastinal Nodal SUVmax and Derived Ratios from 18F-FDG PET/CT Imaging to Predict Nodal Metastases in Non-Small Cell Lung Cancer. Diagnostics (Basel) 2023; 13:1209. [PMID: 37046427 PMCID: PMC10093125 DOI: 10.3390/diagnostics13071209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 04/14/2023] Open
Abstract
18F-FDG positron emission tomography with computed tomography (PET/CT) is a standard imaging modality for the nodal staging of non-small cell lung cancer (NSCLC). To improve the accuracy of pre-operative staging, we compare the staging accuracy of mediastinal lymph node (LN) standard uptake values (SUV) with four derived SUV ratios based on the SUV values of primary tumours (TR), the mediastinal blood pool (MR), liver (LR), and nodal size (SR). In 2015-2017, 53 patients (29 women and 24 men, mean age 67.4 years, range 53-87) receiving surgical resection have pre-operative evidence of mediastinal nodal involvement (cN2). Among these, 114 mediastinal nodes are resected and available for correlative PET/CT analysis. cN2 status accuracy is low, with only 32.5% of the cN2 cases confirmed pathologically. Using receiver operating characteristic (ROC) curve analyses, a SUVmax of N2 LN performs well in predicting the presence of N2 disease (AUC, 0.822). Based on the respective selected thresholds for each ROC curve, normalisation of LN SUVmax to that for mediastinum, liver and tumour improved sensitivities of LN SUVmax from 68% to 81.1-89.2% while maintaining acceptable specificity (68-70.1%). In conclusion, normalised SUV ratios (particularly LR) improve current pre-operative staging performance in detecting mediastinal nodal involvement.
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Affiliation(s)
- Maha AlRasheedi
- School of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, UK; (M.A.)
- West of Scotland PET Centre, Gartnavel General Hospital, NHS Greater Glasgow and Clyde, Glasgow G12 0YN, UK
| | - Sai Han
- West of Scotland PET Centre, Gartnavel General Hospital, NHS Greater Glasgow and Clyde, Glasgow G12 0YN, UK
| | - Helene Thygesen
- School of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, UK; (M.A.)
| | - Matt Neilson
- Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK
| | - Fraser Hendry
- West of Scotland PET Centre, Gartnavel General Hospital, NHS Greater Glasgow and Clyde, Glasgow G12 0YN, UK
| | - Ahmed Alkarn
- Department of Respiratory Medicine, Glasgow Royal Infirmary, NHS Greater Glasgow and Clyde, Glasgow G4 0SF, UK
| | - John D. Maclay
- Department of Respiratory Medicine, Glasgow Royal Infirmary, NHS Greater Glasgow and Clyde, Glasgow G4 0SF, UK
| | - Hing Y. Leung
- School of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, UK; (M.A.)
- Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK
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Liu Y, Hua J, Liu L, Zhang W, Xu S, Chen X. The value of the SUV ratio between lymph node and bone marrow in predicting pelvic lymphatic metastasis of patients with locally advanced cervical cancer: an integrated PET/CT study. Nucl Med Commun 2022; 43:1155-1160. [PMID: 36003034 PMCID: PMC9575580 DOI: 10.1097/mnm.0000000000001613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/16/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE This study aimed to evaluate the value of the standardized uptake value (SUV) ratio between lymph nodes and bone marrow (BM) measured by Fluorine-18-fluorodeoxyglucose PET and computed tomography ( 18 F-FDG PET/CT) for predicting pelvic lymph node (PLN) metastasis in patients with locally advanced cervical cancer (LACC). MATERIALS AND METHODS A total of 62 patients with pathological stage Ib-IVa cervical cancer who underwent 18 F-FDG PET/CT before treatment were reviewed retrospectively. We measured the metabolic and morphological parameters of lymph nodes and primary tumors, bone marrow SUV (SUVBM) and calculated the ratio of lymph nodes maximum SUV (SUVmax) to bone marrow SUV (SUVLN/BM) and the ratio of short-axis diameter to long-axis diameter (Ds/l) of lymph nodes. A receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic efficacy of each parameter. RESULTS There were 180 lymph nodes with pathological evidence included in the study. Our results indicated that Ds/l, SUVmax of lymph nodes (SUVLN) and SUVLN/BM were independent risk factors for PLN metastasis in LACC ( P < 0.05), and SUVLN/BM showed the best diagnostic performance by ROC curve analysis. The SUVBM in the anemia group was significantly higher than that in the nonanemia group (3.05 vs. 2.40, P < 0.05); furthermore, false-positive cases decreased when the SUVLN/BM was used as the diagnostic criterion instead of SUVLN, especially in the anemia group. ROC curve analysis showed that the area under the curve value of the combination of SUVLN/BM and Ds/l was 0.884 ( P < 0.05), which was higher than Ds/l or SUVLN/BM alone. CONCLUSIONS SUVLN/BM could improve the ability to predicting PLN metastasis in patients with LACC, and the diagnostic efficacy of the combination of SUVLN/BM and Ds/l might be better than that of a single parameter.
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Affiliation(s)
- Ying Liu
- Department of Nuclear Medicine, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
| | - Jun Hua
- Department of Nuclear Medicine, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
| | - Lisheng Liu
- Department of Nuclear Medicine, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
| | - Wei Zhang
- Department of Nuclear Medicine, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
| | - Shufan Xu
- Department of Nuclear Medicine, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
| | - Xiaoliang Chen
- Department of Nuclear Medicine, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
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Tian A, Lin R, Yu J, Zhang F, Zheng Q, Yuan X, Sun Z, Zhong Z. The differential diagnostic value of dual-phase 18F-DCFPyL PET/CT in prostate carcinoma. Prostate Cancer Prostatic Dis 2022; 25:351-358. [PMID: 35422099 PMCID: PMC9184273 DOI: 10.1038/s41391-022-00534-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 03/21/2022] [Accepted: 03/29/2022] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Binding of 18F-DCFPyL at prostate cancer (PC) cells increases over time. The dual-phase protocol may be helpful in separating benign lesions from malignant ones associated with prostate cancer. The purpose of this study was to retrospectively analyze the incremental diagnostic value of 18F-DCFPyL dual-time imaging in patients with prostate cancer. METHOD 114 prostate-related malignant lesions and 43 benign lesions in 38 patients with prostate cancer were retrospectively analyzed. Maximum standardized uptake value (SUVmax) for benign and prostate-related malignant lesions were calculated at min 60 and min 120 of PET/CT imaging. In order to calculate SUV ratio, the SUVmax of left gluteus maximus was measured at the same time. The difference of SUVmax metrics and SUV ratio between malignant and benign lesions was statistically analyzed, the cut-off value of ROC curve was calculated, and the diagnostic efficacy of SUVmax index and SUV ratio at two time points was compared. RESULTS SUVmax metrics and SUV ratio of early and delayed imaging of PC-related malignant lesions were significantly higher than those of benign lesions (p < 0.05). In terms of individual indicators, the highest accuracy and sensitivity was in the delayed SUV ratio (89.2% and 94.7%), the best specificity was in the early SUVmax (93.0%). When the individual and combined indicators were compared together, the SUV ratio in the delay period still showed the best diagnostic sensitivity and accuracy, and the best specificity were SUVmax early and ▵SUVmax, SUVmax early and RI. CONCLUSIONS Uptake of 18F-DCFPyL increased over time in prostate-associated malignant lesions compared with benign tissue. For single-phase imaging, 2-hour (delayed) imaging has better diagnostic performance. However, the dual-phase imaging (1 and 2 h) are helpful in the differential diagnosis of prostate-associated malignant lesions and benign lesions.
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Affiliation(s)
- Aijuan Tian
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, People's Republic of China.
| | - Runlong Lin
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - Jing Yu
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - Fan Zhang
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - Qiang Zheng
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - Xin Yuan
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - Zhanhua Sun
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - Zhaoyan Zhong
- Department of Nuclear Medicine, The Second Hospital of Dalian Medical University, Dalian, People's Republic of China
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Hua J, Li L, Liu L, Liu Q, Liu Y, Chen X. The diagnostic value of metabolic, morphological and heterogeneous parameters of 18F-FDG PET/CT in mediastinal lymph node metastasis of non-small cell lung cancer. Nucl Med Commun 2021; 42:1247-1253. [PMID: 34269750 DOI: 10.1097/mnm.0000000000001456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To investigate the value of PET/CT metabolic, morphological and heterogeneous parameters in the diagnosis of 18F-FDG positive mediastinal lymph node metastasis in non-small cell lung cancer (NSCLC). PATIENTS AND METHODS A total of 156 patients with pathologically diagnosed NSCLC and underwent 18F-FDG PET/CT scans were enrolled in this study. Mediastinal lymph nodes with 18F-FDG uptake greater than the mediastinum were analyzed. The metabolic parameters of maximum and mean standardized uptake value (SUVmax, SUVmean), SUVratio (node SUVmax/mediastinum SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), morphological parameters of maximum short diameter (Dmin), CT values and metabolic heterogeneity parameter of coefficient of variation (COV) were measured. The performance of each parameter and their combinations for diagnosis of lymph node metastasis was evaluated through receiver operating characteristic (ROC) curves and binary logistic regression analysis. RESULTS There were 206 lymph nodes with pathological evidence included in the study, including 103 metastatic and 103 nonmetastatic nodes. The SUVmax, SUVmean, SUVratio, TLG, COV and Dmin of metastatic lymph nodes were significantly higher/greater than those in nonmetastatic ones (P < 0.05). ROC curve analysis revealed that the combination of SUVratio, Dmin and COV showed the highest diagnostic efficacy among all single and combined parameters, the area under the curve (AUC) was 0.907 (P = 0.000), these three parameters all increased the risk of lymph node metastasis, with odds ratios of 1.848, 1.293 and 1.258, respectively (all P < 0.05). CONCLUSION Heterogeneity parameter was helpful for the accurate distinction of mediastinal lymph node metastasis in NSCLC. The combination of the SUVratio, Dmin and COV could improve the diagnostic accuracy. Multiple-parameters analysis plays an important complementary role in the diagnosis of lymph node metastasis.
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Affiliation(s)
- Jun Hua
- Department of Nuclear Medicine
| | - Lan Li
- Department of Radiology, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030, P.R. China
| | | | - Qi Liu
- Department of Nuclear Medicine
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Pencharz D, Matei N, Jeon J, Myerson J, Marchbank ND. Identification of specific SUVmax ratios enhances diagnostic accuracy for staging of intrathoracic nodes in lung cancer. Nucl Med Commun 2021; 42:1130-1134. [PMID: 34528931 DOI: 10.1097/mnm.0000000000001441] [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: 11/26/2022]
Abstract
INTRODUCTION Studies demonstrating limited accuracy of 'positive' and 'negative' lymph nodes on fluorodeoxyglucose (FDG) PET-CT in staging for lung cancer have led to guidelines stating mediastinal nodes enlarged on computed tomography, irrespective of FDG uptake, require endobronchial ultrasound (EBUS)-transbronchial needle aspiration (TBNA). However FDG uptake occurs on a continuous spectrum and the use of standardised uptake value (SUV)max ratios, rather than a binary classification, may have improved diagnostic accuracy. METHODS This was a retrospective analysis of patients with lung cancer who had PET-CT and EBUS-TBNA in 2015-2018. Results from EBUS and the SUVmax ratio of sampled lymph nodes to mediastinal blood pool (SUVmax LN/MBP) were analysed. RESULTS From 99 patients 102 malignant and 54 benign nodes were identified. The SUVmax range was 2.5-52 for malignant and 1.6-5.4 for benign nodes. The SUVmax LN/MBP was 1.3-23 for malignant and 0.7-2.3 for benign nodes. All nodes with SUVmax LN/MBP <1.3 were benign with 100% negative predictive value (NPV). All nodes with SUVmax LN/MBP >2.3 were malignant with 100% positive predictive value (PPV). CONCLUSION In this relatively small sample, SUVmax LN/MBP <1.3 had a NPV of 100% for excluding malignant nodes and SUVmax LN/MBP >2.3 had a PPV of 100% for diagnosing malignant nodes. Using SUVmax ratios could obviate the need for staging EBUS in selected patients with resultant time and cost savings. Selecting different SUVmax ratios, chosen to provide high accuracies for the parameter of interest to change management, is a potentially powerful diagnostic tool that is overlooked when FDG uptake is only classified as 'positive' or 'negative'.
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Affiliation(s)
- Deborah Pencharz
- Department of Nuclear Medicine, Royal Free London NHS Foundation Trust, University College London, London, UK
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10
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Fu Y, Xi X, Tang Y, Li X, Ye X, Hu B, Liu Y. Development and validation of tumor-to-blood based nomograms for preoperative prediction of lymph node metastasis in lung cancer. Thorac Cancer 2021; 12:2189-2197. [PMID: 34165236 PMCID: PMC8327690 DOI: 10.1111/1759-7714.14066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 12/21/2022] Open
Abstract
Background To develop and validate tumor‐to‐blood based nomograms for preoperative prediction of lymph node (LN) metastasis in patients with lung cancer (LC). Methods A prediction model was developed in a primary cohort comprising 330 LN stations from patients with pathologically confirmed LC, these data having been gathered from January 2016 to June 2019. Tumor‐to‐blood variables of LNs were calculated from positron emission tomography‐computed tomography (PET‐CT) images of LC and the short axis diameters of LNs were measured on CT images. Tumor‐to‐blood variables, number of stations suspected of harboring LN metastasis according to PET, and independent clinicopathological risk factors were included in the final nomograms. After being internally validated, the nomograms were used to assess an independent validation cohort containing 101 consecutive LN stations accumulated from July 2019 to March 2020. Results Four tumor‐to‐blood variables (left atrium, inferior vena cava, liver, and aortic arch) and the maximum standardized uptake value (SUVmax) for LNs were found to be significantly associated with LN status (p < 0.001 for both primary and validation cohorts). Five predictive nomograms were built. Of these, one with LN SUVmax/left atrium SUVmax was found to be optimal for predicting LN status with AUC 0.830 (95% confidence interval [CI]: 0.774–0.886) in the primary cohort and AUC 0.865 (95% CI: 0.782–0.948) in the validation cohort. All models showed good discrimination, with a modest C‐index, and good calibration in both primary and validation cohorts. Conclusions We have developed tumor‐to‐blood based nomograms that incorporate identified clinicopathological risk factors and facilitate preoperative prediction of LN metastasis in LC patients.
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Affiliation(s)
- Yili Fu
- Department of Thoracic Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Xiaoying Xi
- Department of Nuclear Medicine, Beijing Chao-Yang Hospital, Beijing, China
| | - Yanhua Tang
- Department of Radiology, Beijing Chao-Yang Hospital, Beijing, China
| | - Xin Li
- Department of Thoracic Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Xin Ye
- Department of Thoracic Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Bin Hu
- Department of Thoracic Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Yi Liu
- Department of Thoracic Surgery, Beijing Chao-Yang Hospital, Beijing, China
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11
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Zheng K, Wang X, Jiang C, Tang Y, Fang Z, Hou J, Zhu Z, Hu S. Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients. Front Med (Lausanne) 2021; 8:673876. [PMID: 34222284 PMCID: PMC8249728 DOI: 10.3389/fmed.2021.673876] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 05/11/2021] [Indexed: 01/08/2023] Open
Abstract
Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery. Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the 18F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance. Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test). Conclusions: The RM based on 18F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions.
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Affiliation(s)
- Kai Zheng
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China.,Positron Emission Tomography/Computed Tomography (PET/CT) Center, Hunan Cancer Hospital, Changsha, China.,The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xinrong Wang
- General Electric (GE) Healthcare (China), Shanghai, China
| | - Chengzhi Jiang
- Positron Emission Tomography/Computed Tomography (PET/CT) Center, Hunan Cancer Hospital, Changsha, China
| | - Yongxiang Tang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Zhihui Fang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Jiale Hou
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Zehua Zhu
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Shuo Hu
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, China
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Yoo J, Cheon M, Park YJ, Hyun SH, Zo JI, Um SW, Won HH, Lee KH, Kim BT, Choi JY. Machine learning-based diagnostic method of pre-therapeutic 18F-FDG PET/CT for evaluating mediastinal lymph nodes in non-small cell lung cancer. Eur Radiol 2020; 31:4184-4194. [PMID: 33241521 DOI: 10.1007/s00330-020-07523-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/08/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVES We aimed to find the best machine learning (ML) model using 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for evaluating metastatic mediastinal lymph nodes (MedLNs) in non-small cell lung cancer, and compare the diagnostic results with those of nuclear medicine physicians. METHODS A total of 1329 MedLNs were reviewed. Boosted decision tree, logistic regression, support vector machine, neural network, and decision forest models were compared. The diagnostic performance of the best ML model was compared with that of physicians. The ML method was divided into ML with quantitative variables only (MLq) and adding clinical information (MLc). We performed an analysis based on the 18F-FDG-avidity of the MedLNs. RESULTS The boosted decision tree model obtained higher sensitivity and negative predictive values but lower specificity and positive predictive values than the physicians. There was no significant difference between the accuracy of the physicians and MLq (79.8% vs. 76.8%, p = 0.067). The accuracy of MLc was significantly higher than that of the physicians (81.0% vs. 76.8%, p = 0.009). In MedLNs with low 18F-FDG-avidity, ML had significantly higher accuracy than the physicians (70.0% vs. 63.3%, p = 0.018). CONCLUSION Although there was no significant difference in accuracy between the MLq and physicians, the diagnostic performance of MLc was better than that of MLq or of the physicians. The ML method appeared to be useful for evaluating low metabolic MedLNs. Therefore, adding clinical information to the quantitative variables from 18F-FDG PET/CT can improve the diagnostic results of ML. KEY POINTS • Machine learning using two-class boosted decision tree model revealed the highest value of area under curve, and it showed higher sensitivity and negative predictive values but lower specificity and positive predictive values than nuclear medicine physicians. • The diagnostic results from machine learning method after adding clinical information to the quantitative variables improved accuracy significantly than nuclear medicine physicians. • Machine learning could improve the diagnostic significance of metastatic mediastinal lymph nodes, especially in mediastinal lymph nodes with low 18F-FDG-avidity.
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Affiliation(s)
- Jang Yoo
- Department of Nuclear Medicine, Veterans Health Service Medical Center, Seoul, South Korea.,Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Miju Cheon
- Department of Nuclear Medicine, Veterans Health Service Medical Center, Seoul, South Korea
| | - Yong Jin Park
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Seung Hyup Hyun
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jae Ill Zo
- Department of Thoracic Surgery and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Kyung-Han Lee
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Byung-Tae Kim
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
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Chen PJ, Yap WK, Chang YC, Tseng CK, Chao YK, Hsieh JCH, Pai PC, Lee CH, Yang CK, Ho ATY, Hung TM. Prognostic value of lymph node to primary tumor standardized uptake value ratio in unresectable esophageal cancer. BMC Cancer 2020; 20:545. [PMID: 32522275 PMCID: PMC7288503 DOI: 10.1186/s12885-020-07044-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 06/04/2020] [Indexed: 01/25/2023] Open
Abstract
Background Unresectable esophageal cancer harbors high mortality despite chemoradiotherapy. Better patient selection for more personalized management may result in better treatment outcomes. We presume the ratio of maximum standardized uptake value (SUV) of metastatic lymph nodes to primary tumor (NTR) in 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (FDG PET/CT) may provide prognostic information and further stratification of these patients. Methods The patients with non-metastatic and unresectable esophageal squamous cell carcinoma (SCC) receiving FDG PET/CT staging and treated by chemoradiotherapy were retrospectively reviewed. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-off value for NTR. Kaplan-Meier method and Cox regression model were used for survival analyses and multivariable analyses, respectively. Results From 2010 to 2016, 96 eligible patients were analyzed. The median follow-up time was 10.2 months (range 1.6 to 83.6 months). Using ROC analysis, the best NTR cut-off value was 0.46 for prediction of distant metastasis. The median distant metastasis-free survival (DMFS) was significantly lower in the high-NTR group (9.5 vs. 22.2 months, p = 0.002) and median overall survival (OS) (9.5 vs. 11.6 months, p = 0.013) was also significantly worse. Multivariable analysis revealed that NTR was an independent prognostic factor for DMFS (hazard ratio [HR] 1.81, p = 0.023) and OS (HR 1.77, p = 0.014). Conclusions High pretreatment NTR predicts worse treatment outcomes and could be an easy-to-use and helpful prognostic factor to provide more personalized treatment for patients with non-metastatic and unresectable esophageal SCC.
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Affiliation(s)
- Po-Jui Chen
- Department of Radiation Oncology and Proton Therapy Center, Linkou Chang Gung Memorial Hospital, 5 Fu-Shin Street, Kwei-Shan, Taoyuan, Taiwan
| | - Wing-Keen Yap
- Department of Radiation Oncology and Proton Therapy Center, Linkou Chang Gung Memorial Hospital, 5 Fu-Shin Street, Kwei-Shan, Taoyuan, Taiwan
| | - Yu-Chuan Chang
- Department of Nuclear Medicine and Molecular Imaging Center, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, No.259, Wenhua 1st Rd., Kwei-Shan, Taoyuan, Taiwan
| | - Chen-Kan Tseng
- Department of Radiation Oncology and Proton Therapy Center, Linkou Chang Gung Memorial Hospital, 5 Fu-Shin Street, Kwei-Shan, Taoyuan, Taiwan
| | - Yin-Kai Chao
- Division of Thoracic Surgery, Department of Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Jason Chia-Hsun Hsieh
- Division of Medical Oncology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Chemical and Materials Engineering, Chang Gung University, No.259, Wenhua 1st Rd., Kwei-Shan, Taoyuan, Taiwan
| | - Ping-Ching Pai
- Department of Radiation Oncology and Proton Therapy Center, Linkou Chang Gung Memorial Hospital, 5 Fu-Shin Street, Kwei-Shan, Taoyuan, Taiwan
| | - Ching-Hsin Lee
- Department of Radiation Oncology and Proton Therapy Center, Linkou Chang Gung Memorial Hospital, 5 Fu-Shin Street, Kwei-Shan, Taoyuan, Taiwan
| | - Chan-Keng Yang
- Division of Medical Oncology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Albert Tsung-Ying Ho
- Department of Nuclear Medicine and Molecular Imaging Center, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Tsung-Min Hung
- Department of Radiation Oncology and Proton Therapy Center, Linkou Chang Gung Memorial Hospital, 5 Fu-Shin Street, Kwei-Shan, Taoyuan, Taiwan. .,Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, No.259, Wenhua 1st Rd., Kwei-Shan, Taoyuan, Taiwan.
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Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images. AJR Am J Roentgenol 2020; 215:192-197. [PMID: 32348182 DOI: 10.2214/ajr.19.22346] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE. The purpose of this study was to assess, by analyzing features of the primary tumor with 18F-FDG PET, the utility of deep machine learning with a convolutional neural network (CNN) in predicting the potential of newly diagnosed non-small cell lung cancer (NSCLC) to metastasize to lymph nodes or distant sites. MATERIALS AND METHODS. Consecutively registered patients with newly diagnosed, untreated NSCLC were retrospectively included in a single-center study. PET images were segmented with local image features extraction software, and data were used for CNN training and validation after data augmentation strategies were used. The standard of reference for designation of N category was invasive lymph node sampling or 6-month follow-up imaging. Distant metastases developing during the study follow-up period were assessed by imaging (CT or PET/CT), in tissue obtained from new suspected sites of disease, and according to the treating oncologist's designation. RESULTS. A total of 264 patients with NSCLC participated in follow-up for a median of 25.2 months (range, 6-43 months). N category designations were available for 223 of 264 (84.5%) patients, and M category for all 264. The sensitivity, specificity, and accuracy of CNN for predicting node positivity were 0.74 ± 0.32, 0.84 ± 0.16, and 0.80 ± 0.17. The corresponding values for predicting distant metastases were 0.45 ± 0.08, 0.79 ± 0.06, and 0.63 ± 0.05. CONCLUSION. This study showed that using a CNN to analyze segmented PET images of patients with previously untreated NSCLC can yield moderately high accuracy for designation of N category, although this may be insufficient to preclude invasive lymph node sampling. The sensitivity of the CNN in predicting distant metastases is fairly poor, although specificity is moderately high.
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Abstract
Molecular imaging with positron emission tomography (PET) using tumour-seeking radiopharmaceuticals has gained wide acceptance in oncology with many clinical applications. The hybrid imaging modality PET/CT (computed tomography) allows assessing molecular as well as morphologic information at the same time. Therefore, PET/CT represents an efficient tool for whole-body staging and re-staging within one imaging modality. In oncology, the glucose analogue 18-F-fluorodeoxyglucose (FDG) is the most widely used PET/CT radiopharmaceutical in clinical routine. FDG PET and FDG PET/CT have been used for staging and re-staging of tumour patients in numerous studies. This chapter will discuss the use and the main indications of FDG PET/CT in oncology with special emphasis on lung cancer, lymphoma, head and neck cancer, melanoma and breast cancer (among other tumour entities). A review of the current literature is given with respect to primary diagnosis, staging and diagnosis of recurrent disease. Besides its integral role in diagnosis, staging and re-staging of disease in oncology, there is increasing evidence that FDG PET/CT can be used for therapy response assessment (possibly influencing therapeutic management and treatment planning) by evaluating tumour control, which will also be discussed in this chapter.
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Affiliation(s)
- Juliane Becker
- Department of Nuclear Medicine, University Medicine Rostock, Gertrudenplatz 1, 18057, Rostock, Germany
| | - Sarah M Schwarzenböck
- Department of Nuclear Medicine, University Medicine Rostock, Gertrudenplatz 1, 18057, Rostock, Germany
| | - Bernd J Krause
- Department of Nuclear Medicine, University Medicine Rostock, Gertrudenplatz 1, 18057, Rostock, Germany.
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Improving diagnostic performance of 18F-FDG-PET/CT for assessment of regional nodal involvement in non-small cell lung cancer. Clin Radiol 2019; 74:818.e17-818.e23. [DOI: 10.1016/j.crad.2019.07.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 07/16/2019] [Indexed: 12/26/2022]
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Effect of adipose tissue volume on prognosis in patients with non-small cell lung cancer. Clin Imaging 2018; 50:308-313. [DOI: 10.1016/j.clinimag.2018.05.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 04/24/2018] [Accepted: 05/01/2018] [Indexed: 12/30/2022]
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