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Yang F, Zhang D, Zhao LH, Mao YR, Mu J, Wang HL, Pang L, Yang SQ, Wei X, Liu CW. Prediction of clear cell renal cell carcinoma ≤ 4cm: visual assessment of ultrasound characteristics versus ultrasonographic radiomics analysis. Front Oncol 2024; 14:1298710. [PMID: 39114306 PMCID: PMC11304449 DOI: 10.3389/fonc.2024.1298710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 07/08/2024] [Indexed: 08/10/2024] Open
Abstract
Objective To investigate the diagnostic efficacy of the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model based on ultrasonographic radiomics for the differentiation of small clear cell Renal Cell Carcinoma (ccRCC) and Renal Angiomyolipoma (RAML). Methods The clinical, ultrasound, and contrast-enhanced CT(CECT) imaging data of 302 small renal tumors (maximum diameter ≤ 4cm) patients in Tianjin Medical University Cancer Institute and Hospital from June 2018 to June 2022 were retrospectively analyzed, with 182 patients of ccRCC and 120 patients of RAML. The ultrasound images of the largest diameter of renal tumors were manually segmented by ITK-SNAP software, and Pyradiomics (v3.0.1) module in Python 3.8.7 was applied to extract ultrasonographic radiomics features from ROI segmented images. The patients were randomly divided into training and internal validation cohorts in the ratio of 7:3. The Random Forest algorithm of the Sklearn module was applied to construct the clinical ultrasound imaging model, ultrasonographic radiomics model, and comprehensive model. The efficacy of the prediction models was verified in an independent external validation cohort consisting of 69 patients, from 230 small renal tumor patients in two different institutions. The Delong test compared the predictive ability of three models and CECT. Calibration Curve and clinical Decision Curve Analysis were applied to evaluate the model and determine the net benefit to patients. Results 491 ultrasonographic radiomics features were extracted from 302 small renal tumor patients, and 9 ultrasonographic radiomics features were finally retained for modeling after regression and dimensionality reduction. In the internal validation cohort, the area under the curve (AUC), sensitivity, specificity, and accuracy of the clinical ultrasound imaging model, ultrasonographic radiomics model, comprehensive model, and CECT were 0.75, 76.7%, 60.0%, 70.0%; 0.80, 85.6%, 61.7%, 76.0%; 0.88, 90.6%, 76.7%, 85.0% and 0.90, 92.6%, 88.9%, 91.1%, respectively. In the external validation cohort, AUC, sensitivity, specificity, and accuracy of the three models and CECT were 0.73, 67.5%, 69.1%, 68.3%; 0.89, 86.7%, 80.0%, 83.5%; 0.90, 85.0%, 85.5%, 85.2% and 0.91, 94.6%, 88.3%, 91.3%, respectively. The DeLong test showed no significant difference between the clinical ultrasound imaging model and the ultrasonographic radiomics model (Z=-1.287, P=0.198). The comprehensive model showed superior diagnostic performance than the ultrasonographic radiomics model (Z=4. 394, P<0.001) and the clinical ultrasound imaging model (Z=4. 732, P<0.001). Moreover, there was no significant difference in AUC between the comprehensive model and CECT (Z=-0.252, P=0.801). Both in the internal and external validation cohort, the Calibration Curve and Decision Curve Analysis showed a better performance of the comprehensive model. Conclusion It is feasible to construct an ultrasonographic radiomics model for distinguishing small ccRCC and RAML based on ultrasound images, and the diagnostic performance of the comprehensive model is superior to the clinical ultrasound imaging model and ultrasonographic radiomics model, similar to that of CECT.
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Affiliation(s)
- Fan Yang
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Dai Zhang
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Li-Hui Zhao
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Yi-Ran Mao
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Jie Mu
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Hai-Ling Wang
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Liang Pang
- Department of Urology, Tianjin Occupational Diseases Precaution and Therapeutic Hospital, Tianjin, China
| | - Shi-Qiang Yang
- Department of Urology, Tianjin First Central Hospital, Tianjin, China
| | - Xi Wei
- Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Tianjin, China
| | - Chun-Wei Liu
- Department of Cardiology, Tianjin Chest Hospital, Tianjin University, Tianjin, China
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Chen J, Yang F, Liu C, Pan X, He Z, Fu D, Jin G, Su D. Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors. Eur J Med Res 2023; 28:609. [PMID: 38115095 PMCID: PMC10729460 DOI: 10.1186/s40001-023-01561-1] [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: 09/03/2022] [Accepted: 11/30/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND This study aimed to identify the diagnostic value of models constructed using computed tomography-based radiomics features for discrimination of benign and early stage malignant ovarian tumors. METHODS The imaging and clinicopathological data of 197 cases of benign and early stage malignant ovarian tumors (FIGO stage I/II), were retrospectively analyzed. The patients were randomly assigned into training data set and validation data set. Radiomics features were extracted from images of plain computed tomography scan and contrast-enhanced computed tomography scan, were then screened in the training data set, and a radiomics model was constructed. Multivariate logistic regression analysis was used to construct a radiomic nomogram, containing the traditional diagnostic model and the radiomics model. Moreover, the decision curve analysis was used to assess the clinical application value of the radiomics nomogram. RESULTS Six textural features with the greatest diagnostic efficiency were finally screened. The value of the area under the receiver operating characteristic curve showed that the radiomics nomogram was superior to the traditional diagnostic model and the radiomics model (P < 0.05) in the training data set. In the validation data set, the radiomics nomogram was superior to the traditional diagnostic model (P < 0.05), but there was no statistically significant difference compared to the radiomics model (P > 0.05). The calibration curve and the Hosmer-Lemeshow test revealed that the three models all had a great degree of fit (All P > 0.05). The results of decision curve analysis indicated that utilization of the radiomics nomogram to distinguish benign and early stage malignant ovarian tumors had a greater clinical application value when the risk threshold was 0.4-1.0. CONCLUSIONS The computed tomography-based radiomics nomogram could be a non-invasive and reliable imaging method to discriminate benign and early stage malignant ovarian tumors.
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Affiliation(s)
- Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Fei Yang
- Department of Clinical Medical, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, People's Republic of China
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Xinwei Pan
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Danhui Fu
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Guanqiao Jin
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
| | - Danke Su
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
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Shehata M, Abouelkheir RT, Gayhart M, Van Bogaert E, Abou El-Ghar M, Dwyer AC, Ouseph R, Yousaf J, Ghazal M, Contractor S, El-Baz A. Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review. Cancers (Basel) 2023; 15:2835. [PMID: 37345172 DOI: 10.3390/cancers15102835] [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: 03/29/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.
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Affiliation(s)
- Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Rasha T Abouelkheir
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | | | - Eric Van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Amy C Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Rosemary Ouseph
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
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Feng S, Gong M, Zhou D, Yuan R, Kong J, Jiang F, Zhang L, Chen W, Li Y. A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm). Transl Oncol 2023; 29:101627. [PMID: 36731307 PMCID: PMC9937807 DOI: 10.1016/j.tranon.2023.101627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/26/2022] [Accepted: 01/15/2023] [Indexed: 02/04/2023] Open
Abstract
RATIONALE AND OBJECTIVES Based on radiomics signature and clinical data, to develop and verify a radiomics nomogram for preoperative distinguish between benign and malignant of small renal masses (SRM). MATERIALS AND METHODS One hundred and fifty-six patients with malignant (n = 92) and benign (n = 64) SRM were divided into the following three categories: category A, typical angiomyolipoma (AML) with visible fat; category B, benign SRM without visible fat, including fat-poor angiomyolipoma (fp-AML), and other rare benign renal tumors; category C, malignant renal tumors. At the same time, one hundred and fifty-six patients included in the study were divided into the training set (n = 108) and test set (n = 48). Respectively from corticomedullary phase (CP), nephrogram phase (NP) and excretory phase (EP) CT images to extract the radiomics features, and the optimal features were screened to establish the logistic regression model and decision tree model, and computed the radiomics score (Rad-score). Demographics and CT findings were evaluated and statistically significant factors were selected to construct a clinical factors model. The radiomics nomogram was established by merging Rad-score and selected clinical factors. The Akaike information criterion (AIC) values and the area under the curve (AUC) were used to compare model discriminant performance, and decision curve analysis (DCA) was used to assess clinical usefulness. RESULTS Seven, fifteen, nineteen, and seventeen distinguishing features were obtained in the CP, NP, EP, and three-phase joint, respectively, and the logistic regression and decision tree models were built based on this features. In the training set, the logistic regression model works better than the decision tree model for distinguishing categories A and B from category C, with the AUC of CP, NP, EP and three-phase joint were 0.868, 0.906, 0.937 and 0.975, respectively. The radiomics nomogram constructed based on the three-phase joint Rad-score and selected clinical factor performed well on the training set (AUC, 0.988; 95% CI, 0.974-1.000) for differentiation of categories A and B from category C. In the test set, the AUC of clinical factors model, radiomics signature and radiomics nomogram for discriminating categories A and B from category C were 0.814, 0.954 and 0.968, respectively; for the identification of category A from category C, the AUC of the three models were 0.789, 0.979, 0.985, respectively; for discriminating category B from category C, the AUC of the three models were 0.853, 0.915, 0.946, respectively. The radiomics nomogram had better discriminative than the clinical factors model in both training and test sets (P < 0.05). The radiomics nomogram (AIC = 40.222) with the lowest AIC value was considered the best model compared with that of the clinical factors model (AIC = 106.814) and the radiomics signature (AIC = 44.224). The DCA showed that the radiomics nomogram have better clinical utility than the clinical factors model and radiomics signature. CONCLUSIONS The logistic regression model has better discriminative performance than the decision tree model, and the radiomics nomogram based on Rad-score of three-phase joint and clinical factors has a good predictive effect in differentiating benign from malignant of SRM, which may help clinicians develop accurate and individualized treatment strategies.
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Affiliation(s)
- Shengxing Feng
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Mancheng Gong
- Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China.
| | - Dongsheng Zhou
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Runqiang Yuan
- Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Jie Kong
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Feng Jiang
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Lijie Zhang
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Weitian Chen
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
| | - Yueming Li
- The First Clinical School of Medicine, Guangdong Medical University, Zhanjiang, China,Department of Urology, The People's Hospital of Zhongshan, Zhongshan, China
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Anai K, Hayashida Y, Ueda I, Hozuki E, Yoshimatsu Y, Tsukamoto J, Hamamura T, Onari N, Aoki T, Korogi Y. The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol 2022; 40:1156-1165. [PMID: 35727458 PMCID: PMC9616757 DOI: 10.1007/s11604-022-01298-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/28/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a support vector machine (SVM) classifier using CT texture-based analysis in differentiating focal-type autoimmune pancreatitis (AIP) and pancreatic duct carcinoma (PD), and to assess the radiologists' diagnostic performance with or without SVM. MATERIALS AND METHODS This retrospective study included 50 patients (20 patients with focal-type AIP and 30 patients with PD) who underwent dynamic contrast-enhanced CT. Sixty-two CT texture-based features were extracted from 2D images of the arterial and portal phase CTs. We conducted data compression and feature selections using principal component analysis (PCA) and produced the SVM classifier. Four readers participated in this observer performance study and the statistical significance of differences with and without the SVM was assessed by receiver operating characteristic (ROC) analysis. RESULTS The SVM performance indicated a high performance in differentiating focal-type AIP and PD (AUC = 0.920). The AUC for all 4 readers increased significantly from 0.827 to 0.911 when using the SVM outputs (p = 0.010). The AUC for inexperienced readers increased significantly from 0.781 to 0.905 when using the SVM outputs (p = 0.310). The AUC for experienced readers increased from 0.875 to 0.912 when using the SVM outputs, however, there was no significant difference (p = 0.018). CONCLUSION The use of SVM classifier using CT texture-based features improved the diagnostic performance for differentiating focal-type AIP and PD on CT.
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Affiliation(s)
- Kenta Anai
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yoshiko Hayashida
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Issei Ueda
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Eri Hozuki
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yuuta Yoshimatsu
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Jun Tsukamoto
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Toshihiko Hamamura
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Norihiro Onari
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555 Japan
| | - Yukunori Korogi
- Department of Radiology, Kyushu Rosai Hospital, Moji Medical Center, 3-1, Higashiminatomachi, Moji-ku, Kitakyushu, Fukuoka 801-8502 Japan
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Roussel E, Capitanio U, Kutikov A, Oosterwijk E, Pedrosa I, Rowe SP, Gorin MA. Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review. Eur Urol 2022; 81:476-488. [PMID: 35216855 PMCID: PMC9844544 DOI: 10.1016/j.eururo.2022.01.040] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/08/2022] [Accepted: 01/21/2022] [Indexed: 01/19/2023]
Abstract
CONTEXT The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management. OBJECTIVE To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization. EVIDENCE ACQUISITION The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr. EVIDENCE SYNTHESIS Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice. CONCLUSIONS A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis. PATIENT SUMMARY Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.
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Affiliation(s)
- Eduard Roussel
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Umberto Capitanio
- Department of Urology, University Vita-Salute, San Raffaele Scientific Institute, Milan, Italy; Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alexander Kutikov
- Division of Urology, Department of Surgery, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, USA
| | - Egbert Oosterwijk
- Department of Urology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences (RIMLS), Nijmegen, The Netherlands
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Advanced Imaging Research Center. University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael A Gorin
- Urology Associates and UPMC Western Maryland, Cumberland, MD, USA; Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Habibollahi P, Sultan LR, Bialo D, Nazif A, Faizi NA, Sehgal CM, Chauhan A. Hyperechoic Renal Masses: Differentiation of Angiomyolipomas from Renal Cell Carcinomas using Tumor Size and Ultrasound Radiomics. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:887-894. [PMID: 35219511 DOI: 10.1016/j.ultrasmedbio.2022.01.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 01/18/2022] [Accepted: 01/21/2022] [Indexed: 06/14/2023]
Abstract
A retrospective single-center study was performed to assess the performance of ultrasound image-based texture analysis in differentiating angiomyolipoma (AML) from renal cell carcinoma (RCC) on incidental hyperechoic renal lesions. Ultrasound reports of patients from 2012 to 2017 were queried, and those with a hyperechoic renal mass <5 cm in diameter with further imaging characterization and/or pathological correlation were included. Quantitative texture analysis was performed using a model including 18 texture features. Univariate logistic regression was used to identify texture variables differing significantly between AML and RCC, and the performance of the model was measured using the area under the receiver operating characteristic (ROC) curve. One hundred thirty hyperechoic renal masses in 127 patients characterized as RCCs (25 [19%]) and AMLs (105 [81%]) were included. Size (odds ratio [OR] = 0.12, 95% confidence interval [CI]: 0.04-0.43, p < 0.001) and 4 of 18 texture features, including entropy (OR = 0.09, 95% CI: 0.01-0.81, p = 0.03), gray-level non-uniformity (OR = 0.12, 95% CI: 0.02-0.72, p = 0.02), long-run emphasis (OR = 0.49, 95% CI: 0.27-0.91, p = 0.02) and run-length non-uniformity (OR = 2.18, 95% CI: 1.14-4.16, p = 0.02) were able to differentiate AMLs from RCCs. The area under the ROC curve for the performance of the model, including texture features and size, was 0.945 (p < 0.001). Ultrasound image-based textural analysis enables differentiation of hyperechoic RCCs from AMLs with high accuracy, which improves further when combined with tumor size.
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Affiliation(s)
- Peiman Habibollahi
- Department of Interventional Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Laith R Sultan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Darren Bialo
- Larchmont Imaging Associates, Larchmont, New Jersey, USA
| | - Abdulrahman Nazif
- Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Nauroze A Faizi
- Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Chandra M Sehgal
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anil Chauhan
- Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
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Wang X, Song G, Jiang H. Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools. Cancer Imaging 2021; 21:47. [PMID: 34225784 PMCID: PMC8259143 DOI: 10.1186/s40644-021-00417-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/28/2021] [Indexed: 11/26/2022] Open
Abstract
Background To investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC). Methods Four-phase (pre-contrast phase [PCP], corticomedullary phase [CMP], nephrographic phase [NP], and excretory phase [EP]) contrast-enhanced CT images of AML.wovf (n = 31) and ccRCC (n = 74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs [small: ROI (1), smaller: ROI (2), large: ROI (3)], and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated. Results Among the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher (AUC = 0.876, 0.849, 0.837 and 0.800). Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC = 0.986). Conclusion The combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features.
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Affiliation(s)
- Xu Wang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China. .,Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China.
| | - Ge Song
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China.,Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China
| | - Haitao Jiang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China.,Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, Zhejiang Province, 310022, People's Republic of China
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Su GY, Xu XQ, Zhou Y, Zhang H, Si Y, Shen MP, Wu FY. Texture analysis of dual-phase contrast-enhanced CT in the diagnosis of cervical lymph node metastasis in patients with papillary thyroid cancer. Acta Radiol 2021; 62:890-896. [PMID: 32757639 DOI: 10.1177/0284185120946711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Computed tomography texture analysis (CTTA) provides objective and quantitative information regarding tumor heterogeneity beyond visual inspection. However, no study has yet used CTTA to differentiate metastatic from non-metastatic cervical lymph node in patients with papillary thyroid cancer (PTC). PURPOSE To evaluate the value of texture analysis of dual-phase contrast-enhanced CT images in diagnosing cervical lymph node metastasis in patients with PTC. MATERIAL AND METHODS Metastatic (n = 27) and non-metastatic (n = 32) cervical lymph nodes were analyzed retrospectively. Texture analyses were performed on both arterial (A) and venous (V) phase CT images. Texture parameters, including mean gray-level intensity, skewness, kurtosis, entropy, and uniformity, were obtained and compared between groups. Receiver operating characteristic (ROC) curves analyses and multivariate logistic regression analysis were used in our study. RESULTS Metastatic lymph nodes showed significantly higher A-mean gray-level intensity, A-entropy, and lower A-kurtosis and V-kurtosis (all P < 0.001) than non-metastatic mimics. The ROC curve analyses indicated that A-kurtosis demonstrated an optimal diagnostic area under the curve (AUC; 0.884) and specificity (92.59%), while the A-mean gray-level intensity showed optimal diagnostic sensitivity (90.62%). Multivariate logistic regression analysis showed that A-mean gray-level intensity (P = 0.006, odds ratio [OR] = 24.297) and V-kurtosis (P = 0.014, OR = 19.651) were the independent predictor for metastatic cervical lymph node. CONCLUSION Dual-phase contrast-enhanced CCTA-especially A-mean gray-level intensity and V-kurtosis-may have the potential to diagnose metastatic cervical lymph node in patients with PTC.
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Affiliation(s)
- Guo-Yi Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yan Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Hao Zhang
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yan Si
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Mei-Ping Shen
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
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Wang XJ, Qu BQ, Zhou JP, Zhou QM, Lu YF, Pan Y, Xu JX, Miu YY, Wang HQ, Yu RS. A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features. Front Oncol 2021; 11:633034. [PMID: 33968732 PMCID: PMC8103199 DOI: 10.3389/fonc.2021.633034] [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: 11/24/2020] [Accepted: 03/31/2021] [Indexed: 11/24/2022] Open
Abstract
Background Renal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging. Methods A total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated. Results Four independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956–0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954–1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854–0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2–4 points; >4 to ≤11 points) significantly increased with increasing scores. Conclusion This scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.
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Affiliation(s)
- Xiao-Jie Wang
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bai-Qiang Qu
- Department of Radiology, Wenling Hospital of Traditional Chinese Medicine, Taizhou, China
| | - Jia-Ping Zhou
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiao-Mei Zhou
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuan-Fei Lu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Pan
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jian-Xia Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - You-You Miu
- Department of Ultrasonic, Wenzhou Central Hospital, Wenzhou, China
| | - Hong-Qing Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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11
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Tang X, Pang T, Yan WF, Qian WL, Gong YL, Yang ZG. The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy. Front Oncol 2021; 11:591502. [PMID: 33747910 PMCID: PMC7973240 DOI: 10.3389/fonc.2021.591502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 01/07/2021] [Indexed: 02/05/2023] Open
Abstract
Background and purpose Radiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown. Methods Computed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan–Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model’s validation. Results Hierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn’t distinguish patients’ prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models. Conclusion We firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.
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Affiliation(s)
- Xin Tang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Pang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei-Feng Yan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wen-Lei Qian
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - You-Ling Gong
- Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhi-Gang Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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12
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Zhang Y, Li X, Lv Y, Gu X. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma. Tomography 2020; 6:325-332. [PMID: 33364422 PMCID: PMC7744193 DOI: 10.18383/j.tom.2020.00039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.
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Affiliation(s)
- Yuhan Zhang
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Xu Li
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Yang Lv
- Department of Anesthesia, The Second Hospital of Jilin University, Changchun, China
| | - Xinquan Gu
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
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13
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Liang P, Xu C, Tan F, Li S, Chen M, Hu D, Kamel I, Duan Y, Li Z. Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis. Cancer Med 2020; 10:595-604. [PMID: 33263225 PMCID: PMC7877354 DOI: 10.1002/cam4.3628] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/19/2020] [Accepted: 11/10/2020] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVES To investigate the diagnostic value of contrast-enhanced computed tomography (CECT) histogram analysis in predicting the World Health Organization (WHO) grade of rectal neuroendocrine tumors (R-NETs). MATERIALS AND METHODS A total of 61 (35 G1, 12 G2, 10 G3, and 4 NECs) patients who underwent preoperative CECT and treated with surgery to be confirmed as R-NETs were included in this study from January 2014 to May 2019. We depicted ROIs and measured the CECT texture parameters (mean, median, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis, and entropy) from arterial phase (AP) and venous phase (VP) images by two radiologists. We calculated intraclass correlation coefficient (ICC) and compared the histogram parameters between low-grade (G1) and higher grade (HG) (G2/G3/NECs) by applying appropriate statistical method. We obtained the optimal parameters to identify G1 from HG using receiver operating characteristic (ROC) curves. RESULTS The capability of AP and VP histogram parameters for differentiating G1 from HG was similar in several histogram parameters (mean, median, 10th, 25th, 75th, and 90th percentiles) (all p < 0.001). Skewness, kurtosis, and entropy on AP images showed no significant differences between G1 and HG (p = 0.853, 0.512, 0.557, respectively). Entropy on VP images was significantly different (p = 0.017) between G1 and HG, however, skewness and kurtosis showed no significant differences (p = 0.654, 0.172, respectively). ROC analysis showed a good predictive performance between G1 and HG, and the 75th (AP) generated the highest area under the curve (AUC = 0.871), followed by the 25th (AP), mean (VP), and median (VP) (AUC = 0.864). Combined the size of tumor and the 75th (AP) generated the highest AUC. CONCLUSIONS CECT histogram parameters, including arterial and venous phases, can be used as excellent indicators for predicting G1 and HG of rectal neuroendocrine tumors, and the size of the tumor is also an important independent predictor.
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Affiliation(s)
- Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chuou Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Fangqin Tan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mingzhen Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ihab Kamel
- Russell H. Morgan Department of Radiology and Radiological Science, the Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Yaqi Duan
- Department of Pathology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Radiomic Features and Machine Learning for the Discrimination of Renal Tumor Histological Subtypes: A Pragmatic Study Using Clinical-Routine Computed Tomography. Cancers (Basel) 2020; 12:cancers12103010. [PMID: 33081400 PMCID: PMC7603020 DOI: 10.3390/cancers12103010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 10/07/2020] [Accepted: 10/14/2020] [Indexed: 02/06/2023] Open
Abstract
Simple Summary This study evaluates how advanced image analyses (radiomic features) and machine learning algorithms can help to distinguish subtypes of kidney tumors in computed tomography (CT) images, which is important for further patient treatment. For 201 patients, the image analyses showed a moderate performance, but robustly performed across various imaging centers and even in cases with suboptimal image quality. In particular, distinguishing one specific subtype of kidney tumor (oncocytomas) from other subtypes proves to be challenging. The algorithms presented in this study can help in the clinical decision-making process for kidney tumor patients, for example, to decide whether to perform kidney surgery or not. Abstract This study evaluates the diagnostic performance of radiomic features and machine learning algorithms for renal tumor subtype assessment in venous computed tomography (CT) studies from clinical routine. Patients undergoing surgical resection and histopathological assessment of renal tumors at a tertiary referral center between 2012 and 2019 were included. Preoperative venous-phase CTs from multiple referring imaging centers were segmented, and standardized radiomic features extracted. After preprocessing, class imbalance handling, and feature selection, machine learning algorithms were used to predict renal tumor subtypes using 10-fold cross validation, assessed as multiclass area under the curve (AUC). In total, n = 201 patients were included (73.7% male; mean age 66 ± 11 years), with n = 131 clear cell renal cell carcinomas (ccRCC), n = 29 papillary RCC, n = 11 chromophobe RCC, n = 16 oncocytomas, and n = 14 angiomyolipomas (AML). An extreme gradient boosting algorithm demonstrated the highest accuracy (multiclass area under the curve (AUC) = 0.72). The worst discrimination was evident for oncocytomas vs. AML and oncocytomas vs. chromophobe RCC (AUC = 0.55 and AUC = 0.45, respectively). In sensitivity analyses excluding oncocytomas, a random forest algorithm showed the highest accuracy, with multiclass AUC = 0.78. Radiomic feature analyses from venous-phase CT acquired in clinical practice with subsequent machine learning can discriminate renal tumor subtypes with moderate accuracy. The classification of oncocytomas seems to be the most complex with the lowest accuracy.
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Razik A, Goyal A, Sharma R, Kandasamy D, Seth A, Das P, Ganeshan B. MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma. Br J Radiol 2020; 93:20200569. [PMID: 32667833 DOI: 10.1259/bjr.20200569] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES To assess the utility of magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma. METHODS After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence. RESULTS Several texture parameters on MRI showed high-class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000. CONCLUSIONS MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma. ADVANCES IN KNOWLEDGE There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard.
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Affiliation(s)
- Abdul Razik
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Ankur Goyal
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Raju Sharma
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | | | - Amlesh Seth
- Departments of Urology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Prasenjit Das
- Departments of Pathology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospital NHS Trust, London, United Kingdom
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Wu G, Xie R, Li Y, Hou B, Morelli JN, Li X. Histogram analysis with computed tomography angiography for discriminating soft tissue sarcoma from benign soft tissue tumor. Medicine (Baltimore) 2020; 99:e18742. [PMID: 31914093 PMCID: PMC6959892 DOI: 10.1097/md.0000000000018742] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
To investigate the feasibility of histogram analysis with computed tomography angiography (CTA) in distinguishing between soft tissue sarcomas and benign soft tissue tumors. Fourty nine patients (23 men, mean age = 44.3 years, age range = 25-64) with pathologically-confirmed soft tissue sarcoma (n = 24) or benign soft tissue tumors (n = 25) in the lower extremities undergoing CTA for tumor evaluation were retrospectively analyzed. Two radiologists separately performed histogram analyses of CT density with CTA images by drawing a region of interest (ROI). The 10th (P10), 25th (P25), 50th (P50), 75th (P75), 90th percentiles (P90), mean, and standard deviations (SD) of measured tumor density were obtained along with measurements of the absolute value of kurtosis (AVK), absolute value of skewness (AVS), and inhomogeneity for each tumor. Intra-class correlation coefficients (ICC) were calculated to determine inter- and intra-reader variability in parameter measurements. The Mann-Whitney U test was used to compare histogram parameters between soft tissue sarcomas and benign soft tissue tumors. Receiver operator characteristic (ROC) curves were constructed to evaluate the accuracy of tumor discrimination. ICC was greater than 0.7 for AVS, AVK, and inhomogeneity, and >0.9 for mean, SD, and all percentile measures. There was no significant difference in P10, P25, P50, P75, P90, mean, or SD between soft tissue sarcomas and benign tumors (P > .05). AVS, AVK, and inhomogeneity were significantly higher in soft tissue sarcomas (P < .05). Areas under the curve (AUC) were 0.81, 0.83, and 0.84 for AVS, AVK, and inhomogeneity respectively. AUC were below 0.6 for mean, SD, and all percentiles.Skewness, kurtosis, and inhomogeneity measurements derived from histogram analysis from CTA distinguish between soft tissue sarcomas and benign soft tissue tumors.
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Affiliation(s)
- Gang Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruyi Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yitong Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bowen Hou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | - Xiaoming Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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