Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation

Liu, Ying; Zhang, Yuwei; Cheng, Run-Fen; Liu, Shichang; Qu, Fangyuan; Yin, Xiao‐Yu; Wang, Qin; Xiao, Bohan; Ye, Zhaoxiang

doi:10.1002/jmri.26192

Cited by 89 publications

(67 citation statements)

References 40 publications

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“…ICC values >0.4, >0.6, >0.8, and >0.9 were regarded as representing moderate, good, very good, and excellent reproducibility, respectively. 26 Overall survival (OS) and disease-free survival (DFS) were calculated for all patients who underwent curative operations for lung adenocarcinoma.…”

Section: Radiomic Analysismentioning

confidence: 99%

Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics

et al. 2020

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Background Because shape or irregularity along the tumor perimeter can result from interactions between the tumor and the surrounding parenchyma, there could be a difference in tumor growth rate according to tumor margin or shape. However, no attempt has been made to evaluate the correlation between margin or shape features and tumor growth. Methods We evaluated 52 lung adenocarcinoma (ADC) patients who had at least two computed tomographic (CT) examinations before curative resection. Volume‐based doubling times (DTs) were calculated based on CT scans, and patients were divided into two groups according to the growth pattern (GP) of their ADCs (gradually growing tumors [GP I] vs. growing tumors with a temporary decrease in DT [GP II]). CT radiomic features reflecting margin characteristics were extracted, and radiomic features reflective of tumor DT were selected. Results Among the 52 patients, 41 (78.8%) were assigned to GP I and 11 (21.2%) to GP II. Of the 94 radiomic features extracted, eccentricity, surface‐to‐volume ratio, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5) were ultimately selected for tumor DT prediction. Selected radiomic features in GP I were surface‐to‐volume ratio, contrast, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5), similar to those for total subjects, whereas the radiomic features in GP II were solidity, energy, and busyness. Conclusions This study demonstrated the potential of margin‐related radiomic features to predict tumor DT in lung ADCs. Key points Significant findings of the study We found a relationship between margin‐related radiomic features and tumor doubling time. What this study adds Margin‐related radiomic features can potentially be used as noninvasive biomarkers to predict tumor doubling time in lung adenocarcinoma and inform treatment strategies.

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Section: Radiomic Analysismentioning

confidence: 99%

Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics

et al. 2020

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“…Invasive biopsy is the "gold standard" for pretreatment evaluation of histologic grade, but is often limited due to sample bias [17]. As a non-invasive approach for characterizing tumors comprehensively, ADC-based radiomics has demonstrated potential for prediction of the histologic grade of giloma [18], cervical cancer [19], and bladder cancer [20]. Therefore, we investigated the predictive ability of radiomics features from ADC maps for the histologic grade of SCCs of the tongue and MF.…”

Section: Discussionmentioning

confidence: 99%

Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the tongue and mouth floor: A preliminary study

Ren

Shi

et al. 2019

Preprint

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Background The assessment of histologic grade plays an important part in prognosis evaluation of squamous cell carcinoma (SCC) of the tongue and mouth floor (MF). This study was performed to ascertain if apparent diffusion coefficient (ADC)-based radiomics can predict histologic grade in SCC of the tongue and MF. Methods This study involved 95 patients (training cohort: n = 63; testing cohort: n = 32), who were categorized as low- and high-grade SCCs according to the pathologic findings. A total of 526 radiomics features were extracted from ADC maps. Radiomics signature was constructed with least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the performance of the radiomic signature in the training and testing cohorts. Results Six features were selected to construct the radiomics signature for predicting histologic grade. The ADC-based radiomics signature performed well in discriminating between low- and high-grade tumors, with AUCs of 0.82 and 0.78 in the training and testing cohorts, respectively. Based on the cutoff value of the training cohort, the radiomics signature achieved accuracies of 0.75 and 0.78, sensitivities of 0.71 and 0.86, and specificities of 0.83 and 0.64 in the training and testing cohorts, respectively. Conclusions The ADC-based radiomics signature could identify histologic grade in SCC of the tongue and MF.

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“…Multiple 3D histopathological grading methods for different tissues have been proposed in the literature, based on magnetic resonance imaging (MRI) [12][13][14][15] , optical imaging 16 , ultrasound 17 , and atomic force microscopy 18 . 3D grading methods could possibly serve as a reference for clinical 3D modalities, as well as higher resolution 3D techniques.…”

Section: Introductionmentioning

confidence: 99%

Automating Three-dimensional Osteoarthritis Histopathological Grading of Human Osteochondral Tissue using Machine Learning on Contrast-Enhanced Micro-Computed Tomography

Rytky

Tiulpin

Frondelius

et al. 2019

Preprint

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AbstractObjectiveTo develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT).DesignOsteochondral cores from 24 total knee arthroplasty patients and 2 asymptomatic cadavers (n = 34, Ø = 2 mm; n = 45, Ø = 4 mm) were imaged using CEμCT with phosphotungstic acid-staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depthwise and subjected to dimensionally reduced Local Binary Pattern-textural feature analysis. Regularized Ridge and Logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (Ø = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (Ø = 4 mm samples). The performance was assessed using Spearman’s correlation, Average Precision (AP) and Area under the Receiver Operating Characteristic Curve (AUC).ResultsHighest performance on cross-validation was observed for SZ, both on Ridge regression (ρ = 0.68, p < 0.0001) and LR (AP = 0.89, AUC = 0.92). The test set evaluations yielded decreased Spearman’s correlations on all zones. For LR, performance was almost similar in SZ (AP = 0.89, AUC = 0.86), decreased in CZ (AP = 0.71→0.62, AUC = 0.77→0.63) and increased in DZ (AP = 0.50→0.83, AUC = 0.72→0.72).ConclusionWe showed that the ML-based automatic 3D histopathological grading of osteochondral samples is feasible from CEμCT. The developed method can be directly applied by OA researchers since the grading software and all source codes are publicly available.

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Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation

Abstract: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Cited by 89 publications

References 40 publications

Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics

Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics

Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the tongue and mouth floor: A preliminary study

Automating Three-dimensional Osteoarthritis Histopathological Grading of Human Osteochondral Tissue using Machine Learning on Contrast-Enhanced Micro-Computed Tomography

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