研究者詳細 - 谷　淳至

論文 - 谷　淳至

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Nakajo M., Horizoe Y., Kawaji K., Jinguji M., Tani A., Fukukura Y., Ohishi M., Yoshiura T. . Application of 123I-MIBG myocardial maximum standardized uptake value to characterize cardiac function in patients with pheochromocytoma: comparison with echocardiography . Japanese Journal of Radiology41 ( 4 ) 437 - 448 2023年4月

記述言語：日本語出版者・発行元：Japanese Journal of Radiology

Purpose: This study examined the usefulness of the maximum standardized uptake value (SUVmax) of myocardial [123I]-metaiodobenzylguanidine ([123I]-MIBG) to characterize myocardial function by comparing it with echocardiographic parameters in patients with pheochromocytoma. Materials and methods: This study included 18 patients with pheochromocytoma who underwent both planar and [123I]-MIBG single-photon emission computed tomography/computed tomography scans and echocardiography before surgery. Myocardial [123I]-MIBG visibility and SUVmax were compared with echocardiographic parameters related to systolic and diastolic functions. The Mann–Whitney U test, Fisher exact test, or Spearman rank correlation assessed differences or relationships between two quantitative variables. Results: On visual analysis, 6 patients showed normal myocardial [123I]-MIBG uptake, whereas 12 patients showed decreased myocardial [123I]-MIBG uptake. No patients showed systolic dysfunction. A significant difference was observed in the incidence of diastolic dysfunction between the groups with normal and decreased uptake (p = 0.009), and left ventricular (LV) diastolic dysfunction was observed in 9 (75%) of 12 patients with decreased myocardial uptake. The myocardial SUVmax was significantly lower in 9 patients with LV diastolic dysfunction than in 9 patients with normal cardiac function (1.67 ± 0.37 vs. 3.03 ± 1.38, p = 0.047). Myocardial SUVmax was positively correlated with septal e′ (early diastolic velocity of septal mitral annulus) (ρ = 0.51, p = 0.031) and negatively correlated with the septal E/e′ ratio (early mitral E-velocity to early diastolic velocity of septal mitral annulus; ρ = − 0.64, p = 0.004), respectively. Conclusions: LV diastolic dysfunction was inversely related to myocardial [123I]-MIBG uptake. Myocardial [123I]-MIBG SUVmax may be useful for characterizing cardiac function in patients with pheochromocytoma. Second abstract. The semiquantitative analysis using the myocardial SUVmax in 123I-MIBG SPECT/CT was found to be potentially useful for characterizing cardiac function in patients with pheochromocytoma.

DOI： 10.1007/s11604-022-01365-z

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PubMed

Nakajo M., Kawaji K., Nagano H., Jinguji M., Mukai A., Kawabata H., Tani A., Hirahara D., Yamashita M., Yoshiura T. . The Usefulness of Machine Learning–Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer . Molecular Imaging and Biology25 ( 2 ) 303 - 313 2023年4月

記述言語：日本語出版者・発行元：Molecular Imaging and Biology

Purpose: To examine whether the machine learning (ML) analyses using clinical and pretreatment 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography ([18F]-FDG-PET)–based radiomic features were useful for predicting prognosis in patients with hypopharyngeal cancer. Procedures: This retrospective study included 100 patients with hypopharyngeal cancer who underwent [18F]-FDG-PET/X-ray computed tomography (CT) before treatment, and these patients were allocated to the training (n=80) and validation (n=20) cohorts. Eight clinical (age, sex, histology, T stage, N stage, M stage, UICC stage, and treatment) and 40 [18F]-FDG-PET–based radiomic features were used to predict disease progression. A feature reduction procedure based on the decrease of the Gini impurity was applied. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were compared using the area under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis. Results: The five most important features for predicting disease progression were UICC stage, N stage, gray level co-occurrence matrix entropy (GLCM_Entropy), gray level run length matrix run length non-uniformity (GLRLM_RLNU), and T stage. Patients who experienced disease progression displayed significantly higher UICC stage, N stage, GLCM_Entropy, GLRLM_RLNU, and T stage than those without progression (each, p<0.001). In both cohorts, the logistic regression model constructed by these 5 features was the best performing classifier (training: AUC=0.860, accuracy=0.800; validation: AUC=0.803, accuracy=0.700). In the logistic regression model, 5-year PFS was significantly higher in patients with predicted non-progression than those with predicted progression (75.8% vs. 8.3%, p<0.001), and this model was only the independent factor for PFS in multivariate analysis (hazard ratio = 3.22; 95% confidence interval = 1.03–10.11; p=0.045). Conclusions: The logistic regression model constructed by UICC, T and N stages and pretreatment [18F]-FDG-PET–based radiomic features, GLCM_Entropy, and GLRLM_RLNU may be the most important predictor of prognosis in patients with hypopharyngeal cancer.

DOI： 10.1007/s11307-022-01757-7

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PubMed

Nakajo Masatoyo, Horizoe Yoshihisa, Kawaji Kodai, Jinguji Megumi, Tani Atsushi, Fukukura Yoshihiko, Ohishi Mitsuru, Yoshiura Takashi . Application of 123I-MIBG myocardial maximum standardized uptake value to characterize cardiac function in patients with pheochromocytoma: comparison with echocardiography(タイトル和訳中) . Japanese Journal of Radiology41 ( 4 ) 437 - 448 2023年4月

Kawaji K., Nakajo M., Shinden Y., Jinguji M., Tani A., Hirahara D., Kitazono I., Ohtsuka T., Yoshiura T. . Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer . Molecular Imaging and Biology 2023年

記述言語：日本語出版者・発行元：Molecular Imaging and Biology

Purpose: To develop and identify machine learning (ML) models using pretreatment clinical and 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography ([18F]-FDG-PET)-based radiomic characteristics to predict disease recurrences in patients with breast cancers who underwent surgery. Procedures: This retrospective study included 112 patients with 118 breast cancer lesions who underwent [18F]-FDG-PET/ X-ray computed tomography (CT) preoperatively, and these lesions were assigned to training (n=95) and testing (n=23) cohorts. A total of 12 clinical and 40 [18F]-FDG-PET-based radiomic characteristics were used to predict recurrences using 7 different ML algorithms, namely, decision tree, random forest (RF), neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine (SVM) with a 10-fold cross-validation and synthetic minority over-sampling technique. Three different ML models were created using clinical characteristics (clinical ML models), radiomic characteristics (radiomic ML models), and both clinical and radiomic characteristics (combined ML models). Each ML model was constructed using the top ten characteristics ranked by the decrease in Gini impurity. The areas under ROC curves (AUCs) and accuracies were used to compare predictive performances. Results: In training cohorts, all 7 ML algorithms except for logistic regression algorithm in the radiomics ML model (AUC = 0.760) achieved AUC values of >0.80 for predicting recurrences with clinical (range, 0.892–0.999), radiomic (range, 0.809–0.984), and combined (range, 0.897–0.999) ML models. In testing cohorts, the RF algorithm of combined ML model achieved the highest AUC and accuracy (95.7% (22/23)) with similar classification performance between training and testing cohorts (AUC: training cohort, 0.999; testing cohort, 0.992). The important characteristics for modeling process of this RF algorithm were radiomic GLZLM_ZLNU and AJCC stage. Conclusions: ML analyses using both clinical and [18F]-FDG-PET-based radiomic characteristics may be useful for predicting recurrence in patients with breast cancers who underwent surgery.

DOI： 10.1007/s11307-023-01823-8

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PubMed

Nakajo M., Jinguji M., Ito S., Tani A., Hirahara M., Yoshiura T. . Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology . Japanese Journal of Radiology 2023年

記述言語：日本語出版者・発行元：Japanese Journal of Radiology

Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.

DOI： 10.1007/s11604-023-01476-1

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PubMed

Nakajo M., Nagano H., Jinguji M., Kamimura Y., Masuda K., Takumi K., Tani A., Hirahara D., Kariya K., Yamashita M., Yoshiura T. . The usefulness of machine-learning-based evaluation of clinical and pretreatment18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer . British Journal of Radiology96 ( 1149 ) 20220772 2023年

記述言語：日本語出版者・発行元：British Journal of Radiology

Objective: To examine whether machine learning (ML) analyses involving clinical and18F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer. Methods: This retrospective study included 49 patients with laryngeal cancer who underwent18F-FDG-PET/CT before treatment, and these patients were divided into the training (n = 34) and testing (n = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 4018F-FDG-PET–based radiomic features were used to predict disease progression and survival. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were used for predicting disease progression. Two ML algorithms (cox proportional hazard and random survival forest [RSF] model) considering for time-to-event outcomes were used to assess progression-free survival (PFS), and prediction performance was assessed by the concordance index (C-index). Results: Tumor size, T stage, N stage, GLZLM_ZLNU, and GLCM_Entropy were the five most important features for predicting disease progression.In both cohorts, the naïve Bayes model constructed by these five features was the best performing classifier (training: AUC = 0.805; testing: AUC = 0.842). The RSF model using the five features (tumor size, GLZLM_ZLNU, GLCM_Entropy, GLRLM_LRHGE and GLRLM_SRHGE) exhibited the highest performance in predicting PFS (training: C-index = 0.840; testing: C-index = 0.808). Conclusion: ML analyses involving clinical and18F-F-DG-PET–based radiomic features may help predict disease progression and survival in patients with laryngeal cancer. Advances in knowledge: ML approach using clinical and18F-FDG-PET–based radiomic features has the potential to predict prognosis of laryngeal cancer.

DOI： 10.1259/bjr.20220772

Scopus

PubMed

Yano E., Nakajo M., Jinguji M., Tani A., Kitazono I., Yoshiura T. . I-131 false-positive uptake in a thymic cyst with expression of the sodium-iodide symporter: A case report . Medicine (United States)101 ( 26 ) e29282 2022年6月

記述言語：日本語出版者・発行元：Medicine (United States)

Rationale: I-131 radioiodine false-positive findings in postoperative patients with differentiated thyroid cancer (DTC) should be recognized to avoid unnecessary therapies. Patient concerns and diagnoses: A 50-year-old man underwent I-131 therapy 3 times, including the initial ablative therapy after total thyroidectomy for papillary thyroid cancer. The initial I-131 posttherapeutic whole-body scintigraphy showed 2 cervical and one superior mediastinal focal I-131 positive uptake lesions. The serum thyroglobulin level was negative every time when the radioiodine therapy was performed. Although the 2 cervical positive uptake lesions disappeared after the second therapy, the superior mediastinal I-131 positive uptake persisted even after the third therapy, and this lesion was suspicion of I-131 therapy-resistant node metastasis. Interventions and outcomes: The lesion was resected, and the pathological diagnosis with immune-histochemical analysis was a thymic cyst with thymic epithelial cells having a weak expression of the sodium-iodide symporter (NIS). Lessons: The false-positive result may be attributed to the NIS expression in the thymic cyst epithelial cells. It is necessary to include a thymic cyst in the differential diagnosis, when I-131 uptake is noted in the superior mediastinal region on I-131 posttherapeutic scans of patients with postoperative DTC. Although the I-131 positive uptake in a thymic cyst may be influenced by the I-131 administered dose and scan timing after I-131 administration, the NIS expression may be essential to the false-positive uptake in a thymic cyst.

DOI： 10.1097/MD.0000000000029282

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PubMed

Nakajo M., Takeda A., Katsuki A., Jinguji M., Ohmura K., Tani A., Sato M., Yoshiura T. . The efficacy of18F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors . British Journal of Radiology95 ( 1134 ) 20211050 2022年6月

記述言語：日本語出版者・発行元：British Journal of Radiology

Objective: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (18F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs). Methods: This retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic18F-FDG-PET/ CT. High-risk TETs (high-risk thymomas and thymic carcinomas) were 52 patients. The 107 PET-based radiomic features, including SUV-related parameters [maximum SUV (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] and 1024 deep-learning features extracted from the convolutional neural network were used to predict the pathological risk subtypes of TETs using six different machine-learning algorithms. The area under the curves (AUCs) were calculated to compare the predictive performances. Results: SUV-related parameters yielded the following AUCs for predicting thymic carcinomas: SUVmax 0.713, MTV 0.442, and TLG 0.479 or high-risk TETs: SUVmax 0.673, MTV 0.533, and TLG 0.539. The bestperforming algorithm was the logistic regression model for predicting thymic carcinomas (AUC 0.900, accuracy 81.0%), and the random forest (RF) model for high-risk TETs (AUC 0.744, accuracy 72.2%). The AUC was significantly higher in the logistic regression model than three SUV-related parameters for predicting thymic carcinomas, and in the RF model than MTV and TLG for predicting high-risk TETs (each; p < 0.05). Conclusion:18F-FDG-PET-based radiomic analysis using a machine-learning approach may be useful for predicting the pathological risk subtypes of TETs. Advances in knowledge: Machine-learning approach using18F-FDG-PET-based radiomic features has the potential to predict the pathological risk subtypes of TETs.

DOI： 10.1259/bjr.20211050

Scopus

PubMed

Nakajo M. . Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients . Abdominal Radiology47 ( 2 ) 838 - 847 2022年2月

Nagano H. . Dual-Energy CT-Derived Electron Density for Diagnosing Metastatic Mediastinal Lymph Nodes in Non-Small Cell Lung Cancer: Comparison With Conventional CT and FDG PET/CT Findings . American Journal of Roentgenology218 ( 1 ) 66 - 74 2022年1月

Nakajo M. . Value of Patlak Ki images from 18F-FDG-PET/CT for evaluation of the relationships between disease activity and clinical events in cardiac sarcoidosis . Scientific Reports11 ( 1 ) 2729 2021年12月

Hozaka Y. . Clinical utility and limitation of diagnostic ability for different degrees of dysplasia of intraductal papillary mucinous neoplasms of the pancreas using 18 F-fluorodeoxyglucose-positron emission tomography/ computed tomography . Cancers13 ( 18 ) 2021年9月

Nakajo M. . Application of a machine learning approach to characterization of liver function using 99mTc-GSA SPECT/CT . Abdominal Radiology46 ( 7 ) 3184 - 3192 2021年

Nakajo M. . Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer . Molecular Imaging and Biology23 ( 5 ) 756 - 765 2021年

Nakajo M. . Application of adrenal maximum standardized uptake value to 131I-6β-iodomethyl-19-norcholesterol SPECT/CT for characterizing unilateral hyperfunctioning adrenocortical masses . European Journal of Radiology133 109397 2020年12月

Nakajo M. . [18F]-FDG-PET/CT and [18F]-FAZA-PET/CT Hypoxia Imaging of Metastatic Thyroid Cancer: Association with Short-Term Progression After Radioiodine Therapy . Molecular Imaging and Biology22 ( 6 ) 1609 - 1620 2020年12月

Masatoyo Nakajo, Megumi Jinguji, Atsushi Tani, Takashi Yoshiura . Application of adrenal maximum standardized uptake value to 131I-6β-iodomethyl-19-norcholesterol SPECT/CT for characterizing unilateral hyperfunctioning adrenocortical masses. . European Radiology 2020年12月査読

Nakajo M. . The clinical value of texture analysis of dual-time-point 18F-FDG-PET/CT imaging to differentiate between 18F-FDG-avid benign and malignant pulmonary lesions . European Radiology30 ( 3 ) 1759 - 1769 2020年3月

Nakajo M. . A Pilot Study of Texture Analysis of Primary Tumor [18F]FDG Uptake to Predict Recurrence in Surgically Treated Patients with Non-small Cell Lung Cancer . Molecular Imaging and Biology21 ( 4 ) 771 - 780 2019年8月

Masayuki Nakajo, Megumi Jinguji, Tetsuya Shinaji, Atsushi Tani, Yoshiaki Nakabeppu, Masayuki Nakajo, Akihiro Nakajo, Shoji Natsugoe, Takashi Yoshiura . 18F-FDG-PET/CT features of primary tumours for predicting the risk of recurrence in thyroid cancer after total thyroidectomy: potential usefulness of combination of the SUV-related, volumetric, and heterogeneous texture parameters. . British Journal of Radiology 2019年2月査読

Nakajo M. . 18 F-FDG-PeT/CT features of primary tumours for predicting the risk of recurrence in thyroid cancer after total thyroidectomy: Potential usefulness of combination of the SuV-related, volumetric, and heterogeneous texture parameters . British Journal of Radiology92 ( 1094 ) 20180620 2019年

Kitajima K. . Response to neoadjuvant chemotherapy for breast cancer judged by PERCIST – multicenter study in Japan . European Journal of Nuclear Medicine and Molecular Imaging45 ( 10 ) 1661 - 1671 2018年9月

Masatoyo Nakajo, Megumi Jinguji, Tetsuya Shinaji, Masayuki Nakajo, Masaya Aoki, Atsushi Tani, Masami Sato, Takashi Yoshiura . Texture analysis of 18F-FDG PET/CT for grading thymic epithelial tumours: usefulness of combining SUV and texture parameters. . British Journal of Radiology 2018年2月査読

Nakajo M. . Texture analysis of 18F-FDG peT/CT for grading thymic epithelial tumours: usefulness of combining SuV and texture parameters . British Journal of Radiology91 ( 1083 ) 20170546 2018年

Nagano H. . A small pancreatic hamartoma with an obstruction of the main pancreatic duct and avid FDG uptake mimicking a malignant pancreatic tumor: a systematic case review . BMC gastroenterology17 ( 1 ) 146 2017年12月

Nakajo M. . A pilot study for texture analysis of 18F-FDG and 18F-FLT-PET/CT to predict tumor recurrence of patients with colorectal cancer who received surgery . European Journal of Nuclear Medicine and Molecular Imaging44 ( 13 ) 2158 - 2168 2017年12月

加治屋より子, 谷淳至, 南立亮, 仮屋圭佑, 神宮司メグミ, 中條正豊, 西俣寛人, 福永秀敏, 中條政敬, 吉浦敬 . PET検診がん発見率と年代や性別、受診回数との関連性　どのような人がFDG-PET検診を受けるとよいか . 臨床放射線62 ( 9 ) 1157 - 1163 2017年9月

出版者・発行元：金原出版(株)

PET検診6256件を対象に、癌発見率と年代や性別、受診回数との関連を検討した。実受診者数は4898人で、うち複数回受検者が710人、内訳は男3562人・女2694人・平均年齢58.0歳であった。162件(167の癌病変、重複癌3人、3重複癌1人を含む)の癌が発見され、癌発見率は延べで3.3%、初回で3.0%、癌病変発見率は延べで2.7%、初回で3.1%、2回目で1.4%であった。男女あわせた年代別発見率の推移では、加齢により有意に癌の発見率が上昇していた。男女別で50歳を境とした2群で年齢と発見率を比較したところ、男性では50歳以上で50歳未満に比べて発見率が高く、女性では有意差はなかった。初回受診者のみの病変発見率をみると、全体と同様に男性で発見率が高く、加齢によりその傾向が顕著であったが、各年代での発見率がより高かった。以上、PET癌検診発見率は受検者の年齢や性別、特に年齢に大きく影響されると考えられた。

Nakajo M. . A pilot study of the diagnostic and prognostic values of FLT-PET/CT for pancreatic cancer: comparison with FDG-PET/CT . Abdominal Radiology42 ( 4 ) 1210 - 1221 2017年4月

Masatoyo Nakajo, Yoriko Kajiya, Atsushi Tani, Megumi Jinguji, Masayuki Nakajo, Toru Nihara, Yoshihiko Fukukura, Takashi Yoshiura . A pilot study of the diagnostic and prognostic values of FLT-PET/CT for pancreatic cancer: comparison with FDG-PET/CT. . Abdominal Radiology 2017年4月査読

Masatoyo Nakajo, Yoriko Kajiya, Atsushi Tani, Megumi Jinguji, Masayuki Nakajo, Takashi Yoshiura . FLT-PET/CT diagnosis of primary and metastatic nodal lesions of gastric cancer: comparison with FDG-PET/CT. . Abdominal Radiology 2016年10月査読

Yoriko Kajiya, Atsushi Tani, Syunji Shimaoka, Sadao Tanaka, Masatoyo Nakajo . Early gastric cancer detected by 2-hour delayed FDG PET with the water gastric distention method. . Clinical Nuclear Medicine 2011年12月査読

Atsushi Tani, Yoriko Kajiya, Masatoyo Nakajo, Masayuki Nakajo . A focal pulmonary F-18 FDG uptake without pulmonary CT abnormality in PET/CT probably due to mislocation of the urine F-18 FDG in the left upper renal calyx. . Clinical Nuclear Medicine 2010年9月査読

Atsushi Tani, Shinsaku Tsuchimochi, Yoshiaki Nakabeppu, Masayuki Nakajo . Bone and Tl-201 scintigraphy in a case of hereditary multiple exostoses. . Clinical Nuclear Medicine 2001年12月査読

Atsushi Tani, Masayuki Nakajo, Shinsaku Tsuchimochi, Yoshiaki Nakabeppu, Tomokazu Umanodan . Adrenal incidentalomas showing unilateral concordant visualization by adrenocortical scintigraphy: comparison with adenomas in Cushing's syndrome. . 2000年10月査読

Atsushi Tani, Masayuki Nakajo . Indium-111-chloride scintigraphy visualized gastric involvement in a case of adult T-cell leukemia. . Annals of Nuclear Medicine 1999年6月査読

Atsushi Tani, Yoshiaki Nakabeppu, Masakazu Kobayashi, Shinsaku Tsuchimochi, Masayuki Nakajo . Numerous sites of increased uptake shown on bone scintigraphy in a case of adult T-cell leukemia. . Annals of Nuclear Medicine 1997年11月査読

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