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Borghesi A, Coviello FL, Scrimieri A, Ciolli P, Ravanelli M, Farina D. Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01648-z. [PMID: 37227661 DOI: 10.1007/s11547-023-01648-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/10/2023] [Indexed: 05/26/2023]
Abstract
PURPOSE Persistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population. MATERIAL AND METHODS We retrospectively selected 60 NSNs with an axial diameter of 6-30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses. RESULTS In multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth. CONCLUSION According to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.
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Affiliation(s)
- Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy.
| | - Felice Leopoldo Coviello
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Alessandra Scrimieri
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Pietro Ciolli
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
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Zhai WY, Wong WS, Duan FF, Liang DC, Gong L, Dai SQ, Wang JY. Distinct Prognostic Factors of Ground Glass Opacity and Pure-Solid Lesion in Pathological Stage I Invasive Lung Adenocarcinoma. World J Oncol 2022; 13:259-271. [PMID: 36406190 PMCID: PMC9635791 DOI: 10.14740/wjon1499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 08/01/2022] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Ground glass opacity (GGO) is associated with favorable survival in lung cancer. However, the relevant evidence of the difference in prognostic factors between GGO and pure-solid nodules for pathological stage I invasive adenocarcinoma (IAC) is limited. We aimed to identify the impact of GGO on survival and find prognostic factor for part-GGO and pure-solid patients. METHODS Between December 2007 and August 2018, patients with pathological stage I IAC were retrospectively reviewed and categorized into the pure-GGO, part-GGO, and pure-solid groups. Survival curves were analyzed by the Kaplan-Meier method and compared by log-rank tests. Least absolute shrinkage and selection operator and Cox regression models were used to obtained prognostic factors for disease-free survival (DFS) and overall survival (OS). RESULTS The number of patients with pure-GGO, part-GGO, and pure-solid was 134, 540, and 396, respectively. Part-GGO patients with consolidation-tumor-ratio (CTR) > 0.75 had similar outcome to those with pure-solid nodules. In part-GGO patients, CTR was negatively associated with OS (P = 0.007) and solid tumor size (STS) was negatively associated with DFS (P < 0.001). Visceral pleural invasion (VPI) was negatively associated with OS (P = 0.040) and DFS (P = 0.002). Sublobectomy was negatively associated with OS (P = 0.008) and DFS (P = 0.005), while extended N1 stations examination was associated with improved DFS (P = 0.005) in pure-solid patients. CONCLUSIONS Though GGO component is a positively prognostic factors of patients with pathological stage I IAC, a small proportion of GGO components is not associated with favorable survival. VPI, STS and CTR are the significant predictors for part-GGO patients. Sublobectomy, especially wedge resection should be used cautiously in pure-solid patients.
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Affiliation(s)
- Wen Yu Zhai
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
- These authors contributed equally to drafting this manuscript
| | - Wing Shing Wong
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
- These authors contributed equally to drafting this manuscript
| | - Fang Fang Duan
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
- These authors contributed equally to drafting this manuscript
| | - Da Chuan Liang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Li Gong
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Shu Qin Dai
- Department of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Jun Ye Wang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
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Azour L, Moore WH, O'Donnell T, Truong MT, Babb J, Niu B, Wimmer A, Kiumehr S, Ko JP. Inter-Reader Variability of Volumetric Subsolid Pulmonary Nodule Radiomic Features. Acad Radiol 2022; 29 Suppl 2:S98-S107. [PMID: 33610452 DOI: 10.1016/j.acra.2021.01.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/02/2021] [Accepted: 01/13/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVE To evaluate the inter-observer consistency for subsolid pulmonary nodule radiomic features. MATERIALS AND METHODS Subsolid nodules were selected by reviewing radiology reports of CT examinations performed December 1, 2015 to April 1, 2016. Patients with CTs at two time points were included in this study. There were 55 patients with subsolid nodules, of whom 14 had two nodules. Of 69 subsolid nodules, 66 were persistent at the second time point, yielding 135 lesions for segmentation. Two thoracic radiologists and an imaging fellow segmented the lesions using a semi-automated volumetry algorithm (Syngo.via Vb20, Siemens). Coefficient of variation (CV) was used to assess consistency of 91 quantitative measures extracted from the subsolid nodule segmentations, including first and higher order texture features. The accuracy of segmentation was visually graded by an experienced thoracic radiologist. Influencing factors on radiomic feature consistency and segmentation accuracy were assessed using generalized estimating equation analyses and the Exact Mann-Whitney test. RESULTS Mean patient age was 71 (38-93 years), with 39 women and 16 men. Mean nodule volume was 1.39mL, range .03-48.2mL, for 135 nodules. Several radiomic features showed high inter-reader consistency (CV<5%), including entropy, uniformity, sphericity, and spherical disproportion. Descriptors such as surface area and energy had low consistency across inter-reader segmentations (CV>10%). Nodule percent solid component and attenuation influenced inter-reader variability of some radiomic features. The presence of contrast did not significantly affect the consistency of subsolid nodule radiomic features. Near perfect segmentation, within 5% of actual nodule size, was achieved in 68% of segmentations, and very good segmentation, within 25% of actual nodule size, in 94%. Morphologic features including nodule margin and shape (each p <0.01), and presence of air bronchograms (p = 0.004), bubble lucencies (p = 0.02) and broad pleural contact (p < 0.01) significantly affected the probability of near perfect segmentation. Stroke angle (p = 0.001) and length (p < 0.001) also significantly influenced probability of near perfect segmentation. CONCLUSIONS The inter-observer consistency of radiomic features for subsolid pulmonary nodules varies, with high consistency for several features, including sphericity, spherical disproportion, and first and higher order entropy, and normalized non-uniformity. Nodule morphology influences the consistency of subsolid nodule radiomic features, and the accuracy of subsolid nodule segmentation.
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Affiliation(s)
- Lea Azour
- Department of Radiology, NYU Langone Health (L.A., W.H.M., J.B., J.P.K.).
| | - William H Moore
- Department of Radiology, NYU Langone Health (L.A., W.H.M., J.B., J.P.K.)
| | | | | | - James Babb
- Department of Radiology, NYU Langone Health (L.A., W.H.M., J.B., J.P.K.)
| | - Bowen Niu
- Department of Radiology, Wake Forest Baptist Health (B.N.)
| | | | | | - Jane P Ko
- Department of Radiology, NYU Langone Health (L.A., W.H.M., J.B., J.P.K.)
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Zhai W, Liang D, Duan F, Wong W, Yan Q, Gong L, Lai R, Dai S, Long H, Wang J. Prognostic Nomograms Based on Ground Glass Opacity and Subtype of Lung Adenocarcinoma for Patients with Pathological Stage IA Lung Adenocarcinoma. Front Cell Dev Biol 2021; 9:769881. [PMID: 34957101 PMCID: PMC8692790 DOI: 10.3389/fcell.2021.769881] [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: 09/02/2021] [Accepted: 11/04/2021] [Indexed: 12/03/2022] Open
Abstract
The value of lung adenocarcinoma (LUAD) subtypes and ground glass opacity (GGO) in pathological stage IA invasive adenocarcinoma (IAC) has been poorly understood, and reports of their association with each other have been limited. In the current study, we retrospectively reviewed 484 patients with pathological stage IA invasive adenocarcinoma (IAC) at Sun Yat-sen University Cancer Center from March 2011 to August 2018. Patients with at least 5% solid or micropapillary presence were categorized as high-risk subtypes. Independent indicators for disease-free survival (DFS) and overall survival (OS) were identified by multivariate Cox regression analysis. Based on these indicators, we developed prognostic nomograms of OS and DFS. The predictive performance of the two nomograms were assessed by calibration plots. A total of 412 patients were recognized as having the low-risk subtype, and 359 patients had a GGO. Patients with the low-risk subtype had a high rate of GGO nodules (p < 0.001). Multivariate Cox regression analysis showed that the high-risk subtype and GGO components were independent prognostic factors for OS (LUAD subtype: p = 0.002; HR 3.624; 95% CI 1.263–10.397; GGO component: p = 0.001; HR 3.186; 95% CI 1.155–8.792) and DFS (LUAD subtype: p = 0.001; HR 2.284; 95% CI 1.448–5.509; GGO component: p = 0.003; HR 1.877; 95% CI 1.013–3.476). The C-indices of the nomogram based on the LUAD subtype and GGO components to predict OS and DFS were 0.866 (95% CI 0.841–0.891) and 0.667 (95% CI 0.586–0.748), respectively. Therefore, the high-risk subtype and GGO components were potential prognostic biomarkers for patients with stage IA IAC, and prognostic models based on these indicators showed good predictive performance and satisfactory agreement between observational and predicted survival.
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Affiliation(s)
- Wenyu Zhai
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Dachuan Liang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Fangfang Duan
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wingshing Wong
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qihang Yan
- Department of Thoracic Surgery, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Li Gong
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Renchun Lai
- Department of Anaesthesiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shuqin Dai
- Department of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hao Long
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Junye Wang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Shi L, Zhao J, Peng X, Wang Y, Liu L, Sheng M. CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review. Eur J Radiol 2021; 144:109956. [PMID: 34563797 DOI: 10.1016/j.ejrad.2021.109956] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/25/2021] [Accepted: 08/28/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To provide an overview of the available studies investigating the use of computer tomography (CT) radiomics features for differentiating invasive adenocarcinomas (IAC) from indolent lung adenocarcinomas presenting as ground-glass nodules (GGNs), to identify the bias of the studies and to propose directions for future research. METHOD PubMed, Embase, Web of Science Core Collection were searched for relevant studies. The studies differentiating IAC from indolent lung adenocarcinomas appearing as GGNs based on CT radiomics features were included. Basic information, patient information, CT-scanner information, technique information and performance information were extracted for each included study. The quality of each study was assessed using the Radiomic Quality Score (RQS) and the Prediction model Risk of Bias Assessment Tool (PROBAST). RESULTS Twenty-eight studies were included with patients ranging from 34 to 794. All of them were retrospective. Patients in three studies were from multiple centers. Most studies segmented regions of interest manually. Pyradiomics and AK software were the most frequently used for features extraction. The number of radiomics features extracted varied from 7 to 10329. Logistic regression was the most frequently chosen model. Entropy was identified as radiomics signature in seven studies. The AUC of included studies ranged from 0.77 to 0.98 in 15 validation sets. The percentage RQS ranged from 3% to 50%. According to PROBAST, the overall risk of bias (ROB) was high in 89.3% (25/28) of included studies, unclear in 7.1% (2/28) of included studies, and low in 3.6% (1/28) of included studies. All studies were low concern regarding the applicability of primary studies to the review question. CONCLUSION CT radiomics-based model is promising and encouraging in differentiating IAC from indolent lung adenocarcinomas, though they require methodological rigor. Well-designed studies are necessary to demonstrate their validity and standardization of methods and results can prompt their use in daily clinical practice.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China; Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Xueqing Peng
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yunpeng Wang
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Lei Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China; School of Basic Medical Sciences, and Academy of Engineering and Technology, Fudan University, Shanghai, China.
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People's Hospital, Nantong, China.
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Caruso D, Pucciarelli F, Zerunian M, Ganeshan B, De Santis D, Polici M, Rucci C, Polidori T, Guido G, Bracci B, Benvenga A, Barbato L, Laghi A. Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia. Radiol Med 2021; 126:1415-1424. [PMID: 34347270 PMCID: PMC8335460 DOI: 10.1007/s11547-021-01402-3] [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: 04/21/2021] [Accepted: 07/12/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. MATERIALS AND METHODS One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann-Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. RESULTS Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). CONCLUSIONS Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.
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Affiliation(s)
- Damiano Caruso
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Francesco Pucciarelli
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Marta Zerunian
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, London, UK
| | - Domenico De Santis
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Carlotta Rucci
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Tiziano Polidori
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Benedetta Bracci
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Benvenga
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Barbato
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
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Wu L, Gao C, Ye J, Tao J, Wang N, Pang P, Xiang P, Xu M. The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules. Eur Radiol 2021; 31:9030-9037. [PMID: 34037830 DOI: 10.1007/s00330-021-07948-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 02/25/2021] [Accepted: 03/25/2021] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To evaluate the ability of CT radiomic features extracted from peritumoral parenchyma of 2 mm and 5 mm distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). METHODS For this retrospective study, 121 lung adenocarcinomas appearing as ground-glass nodules on thin-section CT were evaluated. Quantitative radiomic features were extracted from the peritumoral parenchymal region of 2 mm and 5 mm on CT imaging, and the radiomic models of External2 and External5 were constructed. The ROC curves were used to evaluate the performance of different models. Differences between the AUCs were evaluated using DeLong's method. RESULTS The radiomic scores of IAC were statistically higher than those of MIA/AIS in both the External2 and External5 models. The AUCs of the External2 and External5 models were 0.882, 0.778 in the training cohort and 0.888, 0.804 in the validation cohort, respectively. The AUC of the External2 model was not statistically different from the External5 model both in the training cohort (p = 0.116) and validation cohort (p = 0.423). CONCLUSIONS The radiomic features extracted from the peritumoral region of 2 mm and 5 mm at thin-section CT showed good predictive values to differentiate the IAC from AIS/MIA. The radiomic features from the peritumoral region of 5 mm provide no additional benefit in distinguishing IAC from MIA/AIS than that of the 2 mm region. KEY POINTS • The radiomic models from various peritumoral lung parenchyma were developed and validated to predict invasiveness of adenocarcinoma. • The peritumoral parenchyma of lung adenocarcinoma may contain useful information. • Radiomics from peritumoral lung parenchyma of 5 mm provides no added efficiency of the prediction for invasiveness of lung adenocarcinoma.
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Affiliation(s)
- Linyu Wu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianfeng Ye
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingying Tao
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Neng Wang
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Ping Xiang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
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Xiong Z, Jiang Y, Che S, Zhao W, Guo Y, Li G, Liu A, Li Z. Use of CT radiomics to differentiate minimally invasive adenocarcinomas and invasive adenocarcinomas presenting as pure ground-glass nodules larger than 10 mm. Eur J Radiol 2021; 141:109772. [PMID: 34022476 DOI: 10.1016/j.ejrad.2021.109772] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 04/12/2021] [Accepted: 05/10/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE This study aimed to develop a model based on radiomics features extracted from computed tomography (CT) images to effectively differentiate between minimally invasive adenocarcinomas (MIAs) and invasive adenocarcinomas (IAs) manifesting as pure ground-glass nodules (pGGNs) larger than 10 mm. METHOD This retrospective study included patients who underwent surgical resection for persistent pGGN between November 2012 and June 2018 and diagnosed with MIAs or IAs. The patients were randomly assigned to the training and test cohorts. The correlation coefficient method and the least absolute shrinkage and selection operator (LASSO) method were applied to select radiomics features useful for constructing a model whose performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). The radiomics model was compared to a standard CT model (shape, volume and mean CT value of the largest cross-section) and the combined radiomics-standard CT model using univariate and multivariate logistic regression analysis. RESULTS The radiomics model showed better discriminative ability (training AUC, 0.879; test AUC, 0.877) than the standard CT model (training AUC, 0.820; test AUC, 0.828). The combined model (training AUC, 0.879; test AUC, 0.870) did not demonstrate improved performance compared with the radiomics model. Radiomics_score was an independent predictor of invasiveness following multivariate logistic analysis. CONCLUSIONS For pGGNs larger than 10 mm, the radiomics model demonstrated superior diagnostic performance in differentiating between IAs and MIAs, which may be useful to clinicians for diagnosis and treatment selection.
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Affiliation(s)
- Ziqi Xiong
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Yining Jiang
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Siyu Che
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Wenjing Zhao
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Yan Guo
- GE Healthcare, Shenyang, China
| | - Guosheng Li
- Department of Pathology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Ailian Liu
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Zhiyong Li
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
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Hu D, Zhen T, Ruan M, Wu L. The value of percentile base on computed tomography histogram in differentiating the invasiveness of adenocarcinoma appearing as pure ground-glass nodules. Medicine (Baltimore) 2020; 99:e23114. [PMID: 33157987 PMCID: PMC7647573 DOI: 10.1097/md.0000000000023114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
To investigate the value of percentile base on computed tomography (CT) histogram analysis for distinguishing invasive adenocarcinoma (IA) from adenocarcinoma in situ (AIS) or micro invasive adenocarcinoma (MIA) appearing as pure ground-glass nodules.A total of 42 cases of pure ground-glass nodules that were surgically resected and pathologically confirmed as lung adenocarcinoma between January 2015 and May 2019 were included. Cases were divided into IA and AIS/MIA in the study. The percentile on CT histogram was compared between the 2 groups. Univariate and multivariate logistic regression were used to determine which factors demonstrated a significant effect on invasiveness. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was used to evaluate the predictive ability of individual characteristics and the combined model.The 4 histogram parameters (25th percentile, 55th percentile, 95th percentile, 97.5th percentile) and the combined model all showed a certain diagnostic value. The combined model demonstrated the best diagnostic performance. The AUC values were as follows: 25th percentile = 0.693, 55th percentile = 0.706, 95th percentile = 0.713, 97.5th percentile = 0.710, and combined model = 0.837 (all P < .05).The percentile of histogram parameters help to improve the ability to radiologically determine the invasiveness of lung adenocarcinoma appearing as pure ground-glass nodules.
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Affiliation(s)
- Dacheng Hu
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine
| | - Tao Zhen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine
| | - Mei Ruan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine
| | - Linyu Wu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University
- The First Clinical Medical College of Zhejiang Chinese Medical University, Zhejiang, Hangzhou, China
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Brioulet J, David A, Sagan C, Cellerin L, Frampas E, Morla O. Percutaneous CT-guided lung biopsy for the diagnosis of persistent pulmonary consolidation. Diagn Interv Imaging 2020; 101:727-732. [PMID: 32811758 DOI: 10.1016/j.diii.2020.07.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 07/15/2020] [Accepted: 07/28/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE The primary objective of this study was to determine the diagnostic accuracy of percutaneous computed tomography (CT)-guided biopsy of persistent pulmonary consolidations. The secondary objective was to determine the complication rate and identify factors affecting diagnostic yield. MATERIALS AND METHODS Two radiologists retrospectively reviewed 98 percutaneous CT-guided biopsies performed in 93 patients (60 men, 33 women; mean age, 62±14.0 (SD) years; range: 18-88 years) with persistent pulmonary consolidations. Final diagnoses were based on surgical outcomes or 12 months clinical follow-up findings. Biopsy results were compared to the final diagnosis to estimate diagnostic yield. RESULTS A final diagnosis was obtained for all patients: 51/93 (54.8%) had malignant lesions, 12/93 (12.9%) specific definite benign lesions (including 9 infections, two pneumoconiosis and one lipoid pneumonia) and 30/93 (32.3%) non-specific benign lesions. CT-guided biopsy had an overall diagnostic yield of 60% (59/98) with a correct diagnosis for 50/51 malignant lesions (diagnostic yield of 98% for malignancy) and for 9/47 benign lesions (diagnostic yield of 19% for benign conditions). Major complications occurred in 4/98 (4%) of lung biopsies (four pneumothoraxes requiring chest tube placement). CONCLUSION Percutaneous CT-guided biopsy is an alternative to endoscopic or surgical biopsy for the diagnosis of persistent consolidation with a low risk of severe complication.
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Affiliation(s)
- J Brioulet
- Department of Radiology, Centre Hospitalier Universitaire de Nantes, Hôtel Dieu, 44093 Nantes Cedex 1, France.
| | - A David
- Department of Radiology, Centre Hospitalier Universitaire de Nantes, Hôtel Dieu, 44093 Nantes Cedex 1, France
| | - C Sagan
- Department of Pathology, Centre Hospitalier Universitaire de Nantes, Hôpital Laënnec, Saint-Herblain, 44000 Nantes, France
| | - L Cellerin
- Department of Pneumology, Centre Hospitalier Universitaire de Nantes, Hôpital Laënnec, Saint-Herblain, 44000 Nantes, France
| | - E Frampas
- Department of Radiology, Centre Hospitalier Universitaire de Nantes, Hôtel Dieu, 44093 Nantes Cedex 1, France; CRCINA, INSERM, CNRS, Université d'Angers, Université de Nantes, 44000 Nantes, France
| | - O Morla
- Department of Radiology, Centre Hospitalier Universitaire de Nantes, Hôtel Dieu, 44093 Nantes Cedex 1, France
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Cao L, Wang Z, Gong T, Wang J, Liu J, Jin L, Yuan Q. Discriminating between bronchiolar adenoma, adenocarcinoma in situ and minimally invasive adenocarcinoma of the lung with CT. Diagn Interv Imaging 2020; 101:831-837. [PMID: 32482582 DOI: 10.1016/j.diii.2020.05.005] [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: 02/26/2020] [Revised: 04/30/2020] [Accepted: 05/05/2020] [Indexed: 02/08/2023]
Abstract
PURPOSE To identify computed tomography (CT) features that may help distinguish bronchiolar adenoma (BA) from lung adenocarcinomas in situ (AIS) and minimally invasive adenocarcinomas (MIA) among lung lesions presenting as ground-glass nodules (GGNs). MATERIALS AND METHODS A total of 140 patients with GGNs confirmed by surgery and pathology, were reviewed retrospectively. There were 68 men and 72 women with a mean age of 64.3±8.9 (SD) years (range: 31 - 85 years). The CT features of BA, AIS, and MIA were analyzed and compared. CT features, including percentage of solid component, maximum diameter of solid component, lesion density, location, margin, shape, pseudo-cavitation, calcification, ill-defined peripheral opacity, and air bronchogram, were analyzed using multivariate logistic regression and receiver operating characteristic curves. RESULTS There were 11/140 (7.9%) patients with BA (mean age, 67.7±7.5 [SD]; range 45 - 77 years), 63/140 (45.0%) patients with AIS (mean age, 62.5±8.6 [SD]; range 36 - 69 years) and 66/140 (47.1%) patients with MIA (mean age, 63.5±7.9 [SD]; range 35 - 72 years). By comparison with AIS and MIA, significantly different CT features of BA included tumor size, solid component diameters, low CT attenuation of the ground-glass component, irregular shape, ill-defined peripheral opacity, pseudo-cavitation, and abnormal pulmonary vein. Ill-defined peripheral opacity (odds ratio, 1.060; 95% confidence interval [CI]: 1.020 - 1.380) and pseudo-cavitation (odds ratio, 1.236; 95% CI: 1.070 - 1.565) were variables independently associated with the diagnosis of BA. CONCLUSION CT provides morphological features that allow differentiating between BA and AIS-MIA among lung lesions presenting as GGNs.
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Affiliation(s)
- L Cao
- Department of Pathology, The Second Hospital of Jilin University, Changchun, 130041 Jilin, China
| | - Z Wang
- Department of Pathology, The Second Hospital of Jilin University, Changchun, 130041 Jilin, China
| | - T Gong
- Department of Radiology, The Second Hospital of Jilin University, Changchun, 130041 Jilin, China
| | - J Wang
- Department of Radiology, The Second Hospital of Jilin University, Changchun, 130041 Jilin, China
| | - J Liu
- Department of Radiology, The Second Hospital of Jilin University, Changchun, 130041 Jilin, China
| | - L Jin
- Department of Radiology, The Second Hospital of Jilin University, Changchun, 130041 Jilin, China.
| | - Q Yuan
- Department of Radiology, The Second Hospital of Jilin University, Changchun, 130041 Jilin, China
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12
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Bereby-Kahane M, Dautry R, Matzner-Lober E, Cornelis F, Sebbag-Sfez D, Place V, Mezzadri M, Soyer P, Dohan A. Prediction of tumor grade and lymphovascular space invasion in endometrial adenocarcinoma with MR imaging-based radiomic analysis. Diagn Interv Imaging 2020; 101:401-411. [PMID: 32037289 DOI: 10.1016/j.diii.2020.01.003] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 12/21/2019] [Accepted: 01/02/2020] [Indexed: 01/07/2023]
Abstract
PURPOSE To evaluate the capabilities of two-dimensional magnetic resonance imaging (MRI)-based texture analysis features, tumor volume, tumor short axis and apparent diffusion coefficient (ADC) in predicting histopathological high-grade and lymphovascular space invasion (LVSI) in endometrial adenocarcinoma. MATERIALS AND METHODS Seventy-three women (mean age: 66±11.5 [SD] years; range: 45-88 years) with endometrial adenocarcinoma who underwent MRI of the pelvis at 1.5-T before hysterectomy were retrospectively included. Texture analysis was performed using TexRAD® software on T2-weighted images and ADC maps. Primary outcomes were high-grade and LVSI prediction using histopathological analysis as standard of reference. After data reduction using ascending hierarchical classification analysis, a predictive model was obtained by stepwise multivariate logistic regression and performances were assessed using cross-validated receiver operator curve (ROC). RESULTS A total of 72 texture features per tumor were computed. Texture model yielded 52% sensitivity and 75% specificity for the diagnosis of high-grade tumor (areas under ROC curve [AUC]=0.64) and 71% sensitivity and 59% specificity for the diagnosis of LVSI (AUC=0.59). Volumes and tumor short axis were greater for high-grade tumors (P=0.0002 and P=0.004, respectively) and for patients with LVSI (P=0.004 and P=0.0279, respectively). No differences in ADC values were found between high-grade and low-grade tumors and for LVSI. A tumor short axis≥20mm yielded 95% sensitivity and 75% specificity for the diagnosis of high-grade tumor (AUC=0.86). CONCLUSION MRI-based texture analysis is of limited value to predict high grade and LVSI of endometrial adenocarcinoma. A tumor short axis≥20mm is the best predictor of high grade and LVSI.
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Affiliation(s)
- M Bereby-Kahane
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France
| | - R Dautry
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France
| | - E Matzner-Lober
- CREST UMR 9194, ENSAE formation continue, 91120 Palaiseau, France
| | - F Cornelis
- Department of Pathology, Hôpital Lariboisière, AP-HP, 75010 Paris, France
| | - D Sebbag-Sfez
- Department of Radiology, Hôpital Lariboisière, AP-HP, 75010 Paris, France
| | - V Place
- Department of Radiology, Hôpital Lariboisière, AP-HP, 75010 Paris, France
| | - M Mezzadri
- Department of Gynecology, Hôpital Lariboisière, AP-HP, 75010 Paris, France
| | - P Soyer
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France; Université de Paris, Descartes-Paris 5, 75006 Paris, France
| | - A Dohan
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France; Université de Paris, Descartes-Paris 5, 75006 Paris, France; Institut Cochin, 75014 Paris, France.
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Hu T, Wang S, E X, Yuan Y, Huang L, Wang J, Shi D, Li Y, Peng W, Tong T. CT Morphological Features Integrated With Whole-Lesion Histogram Parameters to Predict Lung Metastasis for Colorectal Cancer Patients With Pulmonary Nodules. Front Oncol 2019; 9:1241. [PMID: 31803619 PMCID: PMC6877751 DOI: 10.3389/fonc.2019.01241] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 10/29/2019] [Indexed: 12/23/2022] Open
Abstract
Purpose: To retrospectively identify the relationships between both CT morphological features and histogram parameters with pulmonary metastasis in patients with colorectal cancer (CRC) and compare the efficacy of single-slice and whole-lesion histogram analysis. Methods: Our study enrolled 196 CRC patients with pulmonary nodules (136 in the training dataset and 60 in the validation dataset). Twenty morphological features of contrast-enhanced chest CT were evaluated. The regions of interests were delineated in single-slice and whole-tumor lesions, and 22 histogram parameters were extracted. Stepwise logistic regression analyses were applied to choose the independent factors of lung metastasis in the morphological features model, the single-slice histogram model and whole-lesion histogram model. The areas under the curve (AUC) was applied to quantify the predictive accuracy of each model. Finally, we built a morphological-histogram nomogram for pulmonary metastasis prediction. Results: The whole-lesion histogram analysis (AUC of 0.888 and 0.865 in the training and validation datasets, respectively) outperformed the single-slice histogram analysis (AUC of 0.872 and 0.819 in the training and validation datasets, respectively) and the CT morphological features model (AUC of 0.869 and 0.845 in the training and validation datasets, respectively). The morphological-histogram model, developed with significant morphological features and whole-lesion histogram parameters, achieved favorable discrimination in both the training dataset (AUC = 0.919) and validation dataset (AUC = 0.895), and good calibration. Conclusions: CT morphological features in combination with whole-lesion histogram parameters can be used to prognosticate pulmonary metastasis for patients with colorectal cancer.
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Affiliation(s)
- TingDan Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - ShengPing Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiangyu E
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ye Yuan
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Lv Huang
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - JiaZhou Wang
- Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - DeBing Shi
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuan Li
- Department of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - WeiJun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Alis D, Guler A, Yergin M, Asmakutlu O. Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI. Diagn Interv Imaging 2019; 101:137-146. [PMID: 31727603 DOI: 10.1016/j.diii.2019.10.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/30/2019] [Accepted: 10/02/2019] [Indexed: 01/24/2023]
Abstract
OBJECTIVE To assess the diagnostic value of machine learning-based texture feature analysis of late gadolinium enhancement images on cardiac magnetic resonance imaging (MRI) for assessing the presence of ventricular tachyarrhythmia (VT) in patients with hypertrophic cardiomyopathy. MATERIALS AND METHODS This retrospective study included 64 patients with hypertrophic cardiomyopathy who underwent cardiac MRI and 24-hour Holter monitoring within 1 year before cardiac MRI. There were 42 men and 22 women with a mean age of 48.13±13.06 (SD) years (range: 20-70 years). Quantitative textural features were extracted via manually placed regions of interest in areas with high and intermediate signal intensity on late gadolinium-chelate enhanced images. Feature selection and dimension reduction were performed. The diagnostic performances of machine learning classifiers including support vector machines, Naive Bayes, k-nearest-neighbors, and random forest for predicting the presence of VT were assessed using the results of 24-hour Holter monitoring as the reference test. All machine learning models were assessed with and without the application of the synthetic minority over-sampling technique (SMOTE). RESULTS Of the 64 patients with hypertrophic cardiomyopathy, 21/64 (32.8%) had VT. Of eight machine learning models investigated, k-nearest-neighbors with SMOTE exhibited the best diagnostic accuracy for the presence or absence of VT. k-nearest-neighbors with SMOTE correctly identified 40/42 (95.2%) VT-positive patients and 40/43 (93.0%) VT-negative patients, yielding 95.2% sensitivity (95% CI: 82.5%-99.1%), 93.0% specificity (95% CI: 79.8%-98.1%) and 94.1% accuracy (95% CI: 88.8%-98%). CONCLUSION Machine learning-based texture analysis of late gadolinium-chelate enhancement-positive areas is a promising tool for the classification of hypertrophic cardiomyopathy patients with and without VT.
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Affiliation(s)
- D Alis
- Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Department of Radiology, Halkali/Istanbul, Turkey.
| | - A Guler
- Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Department of Cardiology, Halkali/Istanbul, Turkey
| | - M Yergin
- Bahcesehir University, Department of Software Engineering and applied sciences, Istanbul, Turkey
| | - O Asmakutlu
- Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Department of Radiology, Halkali/Istanbul, Turkey
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Prediction of Lymph Node Maximum Standardized Uptake Value in Patients With Cancer Using a 3D Convolutional Neural Network: A Proof-of-Concept Study. AJR Am J Roentgenol 2019; 212:238-244. [DOI: 10.2214/ajr.18.20094] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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16
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Lee JH, Kim TH, Lee S, Han K, Byun MK, Chang YS, Kim HJ, Lee GD, Park CH. High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules. PLoS One 2018; 13:e0205490. [PMID: 30335856 PMCID: PMC6193644 DOI: 10.1371/journal.pone.0205490] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 09/26/2018] [Indexed: 12/18/2022] Open
Abstract
Objectives To evaluate and compare the diagnostic accuracy of high versus low attenuation thresholds for determining the solid component of ground-glass opacity nodules (GGNs) for the differential diagnosis of adenocarcinoma in situ (AIS) from minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA). Methods Eighty-six pathologically confirmed GGNs < 3 cm observed in 86 patients (27 male, 59 female; mean age, 59.3 ± 11.0 years) between January 2013 and December 2015 were retrospectively included. The solid component of each GGN was defined using two different attenuation thresholds: high (-160 Hounsfield units [HU]) and low (-400 HU). According to the presence or absence of solid portions, each GGN was categorized as a pure GGN or part-solid GGN. Solid components were regarded as indicators of invasive foci, suggesting MIA or IA. Results Among the 86 GGNs, there were 57 cases of IA, 19 of MIA, and 10 of AIS. Using the high attenuation threshold, 44 were categorized as pure GGNs and 42 as part-solid GGNs. Using the low attenuation threshold, 13 were categorized as pure GGNs and 73 as part-solid GGNs. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for the invasive focus were 55.2%, 100%, 100%, 22.7%, and 60.4%, respectively, for the high attenuation threshold, and 93.4%, 80%, 97.2%, 61.5%, and 91.8%, respectively, for the low attenuation threshold. Conclusion The low attenuation threshold was better than the conventional high attenuation threshold for determining the solid components of GGNs, which indicate invasive foci.
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Affiliation(s)
- Jae Ho Lee
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
| | - Tae Hoon Kim
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
| | - Sungsoo Lee
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min Kwang Byun
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yoon Soo Chang
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyung Jung Kim
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Geun Dong Lee
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
- * E-mail: (GDL); (CHP)
| | - Chul Hwan Park
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University college of Medicine, Seoul, Republic of Korea
- * E-mail: (GDL); (CHP)
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Li J, Lu J, Liang P, Li A, Hu Y, Shen Y, Hu D, Li Z. Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole-tumor CT texture analysis as quantitative biomarkers. Cancer Med 2018; 7:4924-4931. [PMID: 30151864 PMCID: PMC6198241 DOI: 10.1002/cam4.1746] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 07/29/2018] [Accepted: 07/30/2018] [Indexed: 12/19/2022] Open
Abstract
Background To explore the application value of computed tomography (CT) texture analysis in differentiating atypical pancreatic neuroendocrine tumors (pNET) from pancreatic ductal adenocarcinomas (PDAC). Materials and methods This single‐center retrospective study was approved by local institutional review board, and the requirement for informed consent was waived. We retrospectively analyzed 127 patients with 50 PDACs and 77 pNETs in pathology database between January 2012 and May 2017.These patients successfully finished preoperative contrast‐enhanced CT test. Texture parameters (mean, median, 5th, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis and entropy) were extracted from portal images and compared between PDAC and 77 pNET groups using proper statistical method. The optimal parameters for differentiating PDACs and atypical pNETs were gained through receiver operating characteristic (ROC) curves. Results On the basis of arterial enhancement, 52 pNETs (67%, 52/77) were typical hypervascular and 25 pNETs (32%, 25/77) were atypical hypovascular. Compared with PDACs, atypical pNETs had statistically higher mean, median, 5th, 10th, and 25th percentiles (P = 0.006, 0.024, 0.000, 0.001, 0.021, respectively) and statistically lower skewness (P = 0.017). However, there were no difference for 75th, 90th percentiles, kurtosis and entropy between these two tumors (P = 0.232, 0.415, 0.143, 0.291, respectively). For differentiating PDACs and atypical pNETs, 5th percentile and 5th+skewness were optimal parameters for alone and combined diagnosis, respectively. Conclusion Volumetric CT texture features, especially combined diagnosis of 5th+skewness can be used as a quantitative tool to distinguish atypical pNETs from PDACs.
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Affiliation(s)
- Jiali Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jingyu Lu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Anqin Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yao Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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