Histogram analysis with computed tomography angiography for discriminating soft tissue sarcoma from benign soft tissue tumor

Wu, Gang; Xie, Ruyi; Li, Yitong; Hou, Bowen; Morelli, John N.; Li, Xiaoming

doi:10.1097/md.0000000000018742

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…[ 35 , 36 ] The AVK, AVS, and variance of histogram parameters reflect the extent of deviation from a normal distribution. [ 37 ] In this study, the variance value was the unique independent predictor of early-stage NPC indicated by the multivariate logistic regression analysis ( P < 0.05). This result indicated that the variance value could reflect the heterogeneity of tissue perfusion on the ASL BF map and plays an important role in distinguishing early-stage NPC from NPLH.…”

Section: Discussionmentioning

confidence: 63%

Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia

Xiao¹,

Wang

Zhao³

et al. 2021

Medicine

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To investigate the feasibility of arterial spin labeling (ASL) blood flow (BF) and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH). Sixty-three stage T1 NPC patients and benign NPLH patients underwent ASL on a 3.0-T magnetic resonance imaging system. BF histogram parameters were derived automatically, including the mean, median, maximum, minimum, kurtosis, skewness, and variance. Absolute values were obtained for skewness and kurtosis (absolute value of skewness [AVS] and absolute value of kurtosis [AVK], respectively). The Mann–Whitney U test, receiver operating characteristic curve, and multiple logistic regression models were used for statistical analysis. The mean, maximum, and variance of ASL BF values were significantly higher in early-stage NPC than in NPLH (all P < 0.0001), while the median and AVK values of early-stage NPC were also significantly higher than those of NPLH (all P < 0.001). No significant difference was found between the minimum and AVS values in early-stage NPC compared with NPLH (P = 0.125 and P = 0.084, respectively). The area under the curve (AUC) of the maximum was significantly higher than those of the mean and median (P < 0.05). The AUC of variance was significantly higher than those of the other parameters (all P < 0.05). Multivariate analysis showed that variance was the only independent predictor of outcome (P < 0.05). ASL BF and its histogram analysis could distinguish early-stage NPC from NPLH, and the variance value was a unique independent predictor.

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Section: Discussionmentioning

confidence: 63%

Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia

Xiao¹,

Wang

Zhao³

et al. 2021

Medicine

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“…The intraclass correlation coefficient (ICC) was the statistical method used in most of the papers reporting a reproducibility analysis [ 13 , 15 – 18 , 20 , 22 – 25 , 27 – 29 , 31 ]. ICC threshold ranged between 0.6 [ 13 ] and 0.9 [ 22 ] for reproducible features.…”

Section: Resultsmentioning

confidence: 99%

“…Eighteen (37%) of the 49 studies included a reproducibility analysis of the radiomic features in their workflow. In 16 (33%) investigations [13,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], the reproducibility of radiomic features was assessed on the basis of repeated segmentations performed by different readers and/or the same reader at different time points. Two (4%) studies presented an analysis to assess the reproducibility based on different acquisition [30] or post-processing [31] techniques.…”

Section: Reproducibility Strategiesmentioning

confidence: 99%

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

et al. 2021

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Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.

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“…Thus, CTA served as an alternative to invasive biopsy in confirming the presence of ischemia. CT perfusion (CTP) parameters, such as blood volume (BV) and blood flow (BF), are able to assess muscle perfusion condition (25,26). Decreased BV or BF is considered to indicate muscle ischemia.…”

Section: Discussionmentioning

confidence: 99%

Radiomics of CTA is feasible in identifying muscle ischemia

Ling¹,

et al. 2022

Acta Radiol

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Background Advanced models based on computed tomography angiography (CTA) radiomics features in discriminating muscle ischemia from normal condition are lacking. Purpose To investigate the feasibility of radiomics of CTA in discriminating ischemic muscle from normal muscle. Material and Methods A total of 102 patients (51 ischemia and 51 non-ischemia) were analyzed using a CTA radiomics method. The radiomics features of muscle were compared between ischemic and normal cases. The maximum relevance minimum redundancy (mRMR) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression model were used. The receiver operating characteristic (ROC) curve was used to determine the performance of radiomics signature. Results Thirty-nine CTA radiomics features were significantly different between the two groups ( P < 0.05). By LASSO, six features were used to construct a model. The signature area under the curve was 0.92 and 0.91 in the training and validation cohorts, respectively. The sensitivity and specificity of the signature were 92% and 86% for the training cohort, and 80% and 94% for the validation cohort, respectively. Conclusion CTA radiomics signature is useful in identifying ischemic muscle in selected patients.

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Histogram analysis with computed tomography angiography for discriminating soft tissue sarcoma from benign soft tissue tumor

Cited by 9 publications

References 28 publications

Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia

Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

Radiomics of CTA is feasible in identifying muscle ischemia

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