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Examples of manually drawing ROIs for cervical squamous cell carcinoma. Panels A–H belong to a 50-year-old female with cervical squamous cell carcinoma of well-differentiated. On panel A (IVIM-DWI at 1200 s/mm²), each radiologist drew ROI-1 (5 mm²) three times to get the values on the maps of ADC, D, D∗, and f, respectively (panels B–E). Panel F is the maximum area of the lesion on T2WI, so the radiologist drew ROI-2 of the whole lesion on panel G. Panel H is the pathological performance, HE ∗ 400.
Source publication
Purpose:
To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and texture analysis on T2-weighted imaging (T2WI) for evaluating pathological differentiation of cervical squamous cell carcinoma.
Method:
This retrospective study included a total of 138 patients with pathologically confirmed poor/moderate/well-...
Similar publications
Background: To investigate the value of diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and amide proton transfer-weighted (APTW) imaging in differentiating the primary glioma from solitary brain metastases (SBM) with the histogram and texture features.
Methods: A total of 65 patients with newly diagnosed glioma or metastases...
Citations
... Mono-exponential DWI, IVIM, and DKI have been widely reported to provide insight into typing and grading of cervical cancer (11,29,30). However, most studies only utilized one or two diffusion models. ...
... Wang et al. found that MD and ADC exhibited significant differences between histological subtypes and FIGO stages of cervical cancer, but not with tumor grades (29). Another study demonstrated that ADC, D, D*, and f were significantly different among the 3 grades of cervical cancer (30). These mixed results could be related to a variety of reasons, such as the reproducibility of DWI metrics, the choice of b values, and different research subjects. ...
Background
Amide proton transfer (APT) imaging has been gradually applied to cervical cancer, yet the relationships between APT and multiple model diffusion-weighted imaging (DWI) have yet to be investigated. This study attempted to evaluate the added value of 3-dimensional (3D) APT imaging to multiple model DWI for assessing prognostic factors of cervical cancer.
Methods
This prospective diagnostic study was conducted in The First Affiliated Hospital of Zhengzhou University. A total of 88 consecutive patients with cervical cancer underwent APT imaging and DWI with 11 b-values (0–2,000 s/mm²). The apparent diffusion coefficient (ADC), pure molecular diffusion (D), perfusion fraction (f), pseudo-diffusion (D*), mean kurtosis (MK), and mean diffusivity (MD) were calculated based on mono-exponential, bi-exponential, and kurtosis models. The mean, minimum, and maximum values of APT signal intensity (APT SI) and DWI-derived metrics were compared based on tumor stages, subtypes, grades, and lymphovascular space invasion status by Student’s t-test or Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the parameters.
Results
APT SImax, APT SImin, MKmean, and MKmax showed significant differences between adenocarcinoma (AC) and squamous cell carcinoma (SCC) (all P<0.05). APT SImean, APT SImax, and MKmax were higher and ADCmin, Dmean, Dmin, and MDmin were lower in the high-grade tumor than in low-grade tumor (all P<0.05). For distinguishing lymphovascular space invasion, only MKmean showed significant difference (P=0.010). APT SImax [odds ratio (OR) =2.347, P=0.029], APT SImin (OR =0.352; P=0.024), and MKmean (OR =6.523; P=0.001) were the independent predictors for tumor subtype, and APT SImax (OR =2.885; P=0.044), MDmin (OR =0.155, P=0.012) were the independent predictors for histological grade of cervical cancer. When APT SImin and APT SImax was combined with MKmean and MKmax, the diagnostic performance was significantly improved for differentiating AC and AC [area under the curve (AUC): 0.908, sensitivity: 87.5%; specificity: 83.3%; P<0.001]. The combination of APT SImean, APT SImax, ADCmin, MKmax, and MDmin demonstrated the highest diagnostic performance for predicting tumor grade (AUC: 0.903, sensitivity: 78.6%; specificity: 88.9%; P<0.001).
Conclusions
Addition of APT to DWI may improve the ability to noninvasively predict poor prognostic factors of cervical cancer.
... In the radiomics signature selected by DC, there existed an original feature termed "Original_GLCM_Correlation_T2", which was a texture feature of the gray-level co-occurrence matrix (GLCM) subgroup in the T2WI sequence. Correlation is a value between 0 (uncorrelated) and 1 (perfectly correlated) that characterizes the correlation between the grayscale values and the respective voxels in the GLCM (36). This feature has previously been described as a robust and independent feature in other clinical scenarios (37,38). ...
Purpose
To predict chromosome 7 gain and chromosome 10 loss (+7/-10) in IDH wild-type (IDH-wt) histologically low-grade gliomas (LGG) by machine learning models based on MRI radiomics and semantic features.
Methods
A total of 122 patients diagnosed as IDH-wt histologically LGG were retrospectively included in this study. The patients were randomly divided into a training group and a test group in a ratio of 7:3. The radiomics features were extracted from axial T1WI, T2WI, FLAIR and CET1 sequences, respectively. The distance correlation (DC) and least absolute shrinkage and selection operator (LASSO) were used to select the radiomics signatures. Three machine learning algorithms including neural network (NN), support vector machine (SVM), and linear discriminant analysis (LDA) were used to construct radiomics models. In addition, a nomogram was developed by combining the optimal radiomics signature with clinical risk factors, and the potential clinical utility of the nomogram was evaluated using decision curve analysis.
Results
The LDA+DC model was identified as the optimal classifier among the six radiomics models. Necrosis was determined as a risk factor for +7/-10 in IDH-wt histologically LGG. The nomogram achieved the best performance, with an AUC of 0.854 and an accuracy of 0.778 in the independent test group. The decision curve of the nomogram confirmed its clinical usefulness in a wide range of thresholds.
Conclusion
The nomogram combining radiomics and semantic features can predict the +7/-10 status effectively, which may contribute to the risk stratification and individualized treatment planning of patients with IDH-wt histologically LGG.
... Noninvasive methods are used to evaluate the internal characteristics of tumours for diagnosis, treatment, and prognosis [20]. Texture analysis has emerged in numerous medical fields in recent years, including the differential diagnosis of benign and malignant mucinous soft tissue tumours [21], the prediction of the pathological grade of breast phyllodes tumours [22], and research on the differentiation and grade of cervical squamous cell carcinoma [23]. Basic first-order statistics are used to describe the pixel intensity and its distribution in the target area as the first-order characteristics of texture. ...
Background
The purpose of this study was to identify neurogenic tumours and pleomorphic adenomas of the parapharyngeal space based on the texture characteristics of MRI-T2WI.
Methods
MR findings and pathological reports of 25 patients with benign tumours in the parapharyngeal space were reviewed retrospectively (13 cases with pleomorphic adenomas and 12 cases with neurogenic tumours). Using PyRadiomics, the texture of the region of interest in T2WI sketched by radiologists was analysed. By using independent sample t-tests and Mann‒Whitney U tests, the selected texture features of 36 Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Dependence Matrix (GLDM) were tested. A set of parameters of texture features showed statistically significant differences between the two groups, which were selected, and the diagnostic efficiency was evaluated via the operating characteristic curve of the subjects.
Results
The differences in the three parameters – small dependence low level emphasis (SDLGLE), low level emphasis (LGLE) and difference variance (DV) of characteristics – between the two groups were statistically significant (P < 0.05). No significant difference was found in the other indices. ROC curves were drawn for the three parameters, with AUCs of 0.833, 0.795, and 0.744, respectively.
Conclusions
There is a difference in the texture characteristic parameters based on magnetic resonance T2WI images between neurogenic tumours and pleomorphic adenomas in the parapharyngeal space. For the differential diagnosis of these two kinds of tumours, texture analysis of significant importance is an objective and quantitative analytical tool.
... The count of runs that have the grey level i and the length j takes place in image ROI is described by the , element of the grey level run length matrix , | . This element is located along the angle [13][14]. ...
Purpose
To evaluate the clinical value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) with mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models for predicting rectal adenomas with canceration.
Material and methods
Sixty patients with postoperative pathology-confirmed rectal adenoma (n = 31) and adenoma with canceration (n = 29) were enrolled and underwent IVIM-DWI scanning. The ME-derived apparent diffusion coefficient (ADC), BE-derived true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), SE-derived distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) were measured. The differences in each parameter between adenoma and canceration were compared. Multivariate binary logistic regression analysis was used to establish models for predicting rectal adenomas with canceration. Receiver operating characteristic curve analysis was applied to evaluate diagnostic performances of each model in terms of sensitivity, specificity, accuracy, and area under the curve (AUC).
Results
The AUCs of ADC, D, D*, f, DDC and α were 0.851 (95 % confidence interval, CI, 0.735–0.930), 0.895 (95 % CI, 0.789–0.960), 0.720 (95 % CI, 0.589–0.828), 0.791 (95 % CI, 0.667–0.886), 0.841 (95 % CI, 0.724–0.923) and 0.738 (95 % CI, 0.608–0.834), respectively. The AUCs of BE and SE models were 0.927 (95 % CI, 0.829–0.978) and 0.874 (95 % CI, 0.763–0.946), respectively. The AUC, sensitivity, specificity, and accuracy of the derived four values (ADC, D, f, and DDC) from the combination of three models were 0.950, 96.6 % (95 % CI, 95.3–97.6 %), 80.6 % (95 % CI, 78.0–82.9 %), and 88.3 % (95 % CI, 86.2–90.2 %), respectively.
Conclusion
ADC can easily and effectively predict rectal adenomas with canceration. The BE model has a better combination of sensitivity and specificity for the diagnosis of rectal adenoma canceration.
