An MR-Based Radiomics Model for Differentiation Between Hepatocellular Carcinoma and Focal Nodular Hyperplasia in Non-Cirrhotic Liver

doi:10.21203/rs.3.rs-159301/v1

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An MR-Based Radiomics Model for Differentiation Between Hepatocellular Carcinoma and Focal Nodular Hyperplasia in Non-Cirrhotic Liver

https://doi.org/10.21203/rs.3.rs-159301/v1

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Purpose:

This study aimed to develop and validate a radiomics model for differentiating between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI).

Methods:

We retrospectively enrolled 149 HCC patients and 75 FNH patients seen between May 2015 and May 2019 at our center and randomly allocated patients to a training set (n = 156) and a validation set (n = 68). A total of 2,260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forests, and the least absolute shrinkage and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the three models was compared.

Results:

Eight radiomics features were chosen to build a radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen to build the clinical model. When evaluating the performance of three models, the clinical model that included clinical data and visual MRI findings achieved excellent performance in the training set (AUC, 0.937; 95% CI, 0.887–0.970) and the validation set (AUC, 0.903; 95% CI, 0.807–0.962), and there was no significant difference between the radiomics model and the clinical model. The AUC of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p = 0.002) and validation (0.972 vs. 0.903, p = 0.032) sets.

Conclusions:

The combined model based on clinical and radiomics features can well distinguish HCC from FNH in non-cirrhotic liver. Our model may assist clinicians in the clinical decision-making process.

Surgery

Oncology

Radiomics

Hepatocellular Carcinoma

Focal Nodular Hyperplasia

Magnetic Resonance Imaging

Focal nodular hyperplasia (FNH) is the second most common benign tumor of the liver, and it is found at autopsy with a prevalence of 0.3–3% [1, 2]. It is often considered a hyperplastic reaction resulting from arterial malformation. Almost 60–80% of cases are asymptomatic and discovered by chance [3, 4]. A typical FNH is a solitary well-defined, unencapsulated mass, with a characteristic ‘spoke-wheel’ central scar that contains dystrophic arterial vessels on computed tomography (CT), magnetic resonance imaging (MRI), and ultrasonography (US) [5]. MRI has a higher sensitivity compared to US and CT and a specificity of almost 100% for the diagnosis of FNH. However, its sensitivity is lower (70–80%), especially in small FNHs where the central scar is often missing. However, it has been reported that 35–70% FNHs do not show this typical imaging feature, and atypical findings, including strong hyper-intensity on T2-weighted imaging, a pseudocapsule that mimics a true capsule, and washout can result in confusion with HCC [6–8]. Furthermore, correct diagnosis of FNH on CT and MRI may not even be possible in about 30% and 20% of cases, respectively, due to atypical radiological features [3, 9]. The hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI (Gd-EOBDTPA-MRI) provides further valuable diagnostic information for differentiation between FNH and HCC. It has been reported that 10–15% of HCCs show iso- or hyperintensity on the HBP, and approximately 73–90% of FNHs show iso- or hyperintensity on the HBP, so differential diagnosis is difficult because of the overlapping features [10].

However, intractable cases must be diagnosed accurately because they require entirely different medical management [11, 12]. Compared to other liver lesions, the diameter of the FNH is stable in most patients and complications are extremely rare [13, 14]. The American College of Gastroenterology (ACG) Clinical Guidelines suggest that asymptomatic FNH does not require intervention [15]. HCC is the most common primary liver cancer and the third most common cause of cancer death worldwide, and interventions must be introduced when the diagnosis is clear [11]. Therefore, in atypical cases that are difficult to diagnose on imaging, especially in a non-cirrhotic liver, liver biopsy is necessary [7]. However, liver biopsy is an invasive procedure and there is potential for pain and other complications [16]. Therefore, other liver lesions, such as hepatocellular adenoma (HCA), should be taken into account in the differential diagnosis. The reported prevalence of HCA is between 0.001–0.004%, and it is approximately 10 times less common than FNH. There are few reports on the differential diagnosis between HCA and other liver lesions. Therefore, HCA was not included in our study because there were not enough cases. In summary, a non-invasive method that can distinguish HCC from FNH is desperately needed.

Radiomics using a large number of quantitative features extracted from images, providing features not available to the naked eye, has been used in tumor molecular classification, differential diagnosis, treatment selection, therapeutic effect detection, and prognosis evaluation. To the best of our knowledge, there are few studies on the differentiation between HCC and FNH on MRI using radiomics methods. This study aimed to develop and validate a radiomics model that is non-invasive and has high accuracy for differentiating HCC from FNH in non-cirrhotic liver.

Patients

In this single-center retrospective study, medical records were viewed to identify all consecutive cases seen between May 2015 and May 2019. The inclusion criteria applied to HCCs were: (a) diagnosis with pathological evidence after surgery; (b) radiological features of liver cirrhosis (cirrhotic liver may demonstrate a nodular surface, widened fissures between lobes, an atrophied right lobe, hypertrophy of the left lobe and/or caudate lobe, and other features including portal vein dilation, portosystemic shunts, splenomegaly, ascites, etc.); (c) no previous history of hepatectomy or radiotherapy; and (d) HCC without blood vessels, bile duct invasion, or distant metastasis radiologically, which strongly supported the diagnosis and there was no need for it to be distinguished from benign disease. The inclusion criteria applied to FNHs were: (a) pathological evidence after surgery or liver biopsy and (b) typical FNH diagnosis according to the EASL Clinical Practice Guidelines [7] (to improve the applicability of the model). The exclusion criteria for both HCCs and FNHs were as follows: (a) absence of high-quality pretreatment Gd-DTPA contrast-enhanced MRI (ceMRI) performed in our center; (b) MRI data obtained at least 2 months prior to the acquisition of pathological evidence; and (c) incomplete medical records and unavailability of the required clinical data (Fig. 1). Up to three imaging studies per patient were included as long as studies were more than 6 months apart.

This study was approved by the Institutional Ethics Committee of our hospital and written informed consent was obtained from all study participants. The studies were performed in accordance with the ethical standards outlined in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

MR Image acquisition and image processing

MRI examinations were performed using a 3.0 T magnetic resonance scanner (Magnetom Verio, Siemens Healthcare, Erlangen, Germany), an 8-channel phased array body coil, and a high-pressure syringe. The contrast agent was Gd-DTPA (Gd-DTPA, BeiLu Pharmaceutical Co., Ltd., Beijing, China), the dosage was 0.2 mL/kg; the speed was 2.5 mL/s, and the follow-up was rinsed with 20 mL normal saline. Preparation before the scan included fasting and no drinking for more than 4 hours, psychological guidance, and breathing training (calm breath-holding at the end of the breath). Contrast-enhanced axial T1-weighted images (CE-T1) were acquired using a three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) sequence (TR = 4.16 ms, TE = 2.01 ms, FOV = 380 × 308 mm, matrix = 320 × 320 × 75%, slice thickness = 3 mm, spacing = 3 mm, FA = 16°, and NEX = 1) with multiphase contrast. Arterial phase, portal venous phase, and delayed phase images were acquired after contrast administration at 20–30, 60–70, and 120–180 s for each patient, with breath-holding in all phases.

The FNH and HCC lesions were segmented manually using a 3D-Slicer (version 4.10.2; http://www.slicer.org). The AP and PVP of T1 images were used to indicate the volumes of interest (VOIs) by drawing the outline of tumor tissue layer-after-layer and avoiding the bile duct and vessels by Radiologists 1 and 2. If there were multiple lesions, only the largest lesions were segmented. PyRadiomics (version 2.1; http://www.radiomics.io/) implementation in 3D-Slicer was utilized for further preprocessing steps and radiomics feature extraction. We adopted resampling as a preprocessing method, which was performed to obtain a voxel size of 1 × 1 × 1 mm³ via trilinear interpolation before feature calculation [17]. A fixed bin width of 25 was used for the image discretization. Image reconstruction was performed by applying wavelet decomposition filtering and Laplacian of Gaussian filtering with sigma values of 0.5, 1.0, and 1.5. Seven common feature groups were extracted from filtered and original images in three dimensions, including a first order, gray-level dependence matrix (GLDM), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighboring gray tone difference matrix (NGTDM), and shape (Fig. 2).

Inter-observer and intra-observer agreement

Inter- and intra-observer correlation coefficients (ICC) were used to evaluate the inter-observer reliability and intra-observer reproducibility of feature extraction [18]. Thirty samples were randomly chosen and delineated by two radiologists. Radiologist 1 delineated the VOIs on arterial phase and portal venous phase of T1 images twice within 1 week under the same standard to assess intra-observer reproducibility, and Radiologist 2 independently delineated the VOIs once to assess inter-observer agreement by comparing the results with the radiomics features extracted from the VOIs delineated by Radiologist 1 [18]. Radiomics features were selected when the ICC was > 0.8. Radiologist 1 finished the remaining samples.

Radiomics feature selection and model construction

Before radiomics feature selection, z-score normalization was employed to eliminate different feature magnitudes by scaling values to a mean of 0 and a standard deviation of 1 [18]. Then, the samples were randomly grouped into training (n = 156) and validation sets (n = 68). The training set was used for radiomics feature selection and construction of the three models, and the validation set was used to evaluate the diagnostic performance of the three models. We used two machine learning algorithms most commonly used for feature filtering and selection without involvement of the model: (i) Max-Relevance and Min-Redundancy (mRMR) and (ii) random forests (RF). Each algorithm selects the top 20 features with the highest score or the highest importance features. Then, a correlation analysis was carried out to exclude the features with high correlation. Least absolute shrinkage and selection operator (LASSO) regression was employed for the next step selection of features, with penalty parameter tuning conducted by 10-fold cross-validation to compile a radiomics signature [18–24]. The optimal radiomics signature was then used to create the radiomics model.

Construction of the clinical model and combined model

Univariate analysis was applied to compare the differences in clinical factors (including clinical information and MR features) between the two groups, and a multiple logistic regression analysis was used to build the clinical model using the significant variables from the univariate analysis as inputs. Odds ratios (ORs) as estimates of relative risk with 95% confidence intervals (CI) were obtained for each risk factor. The combined model was built using the clinical factors in the clinical model and Rad score in the radiomics model.

Statistics

Statistical analysis was performed using R (version 3.6.3; R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were compared using the χ² test or Fisher’s exact test. Continuous variables were expressed as the median [Q1, Q3] and compared using the Student’s t-test or Mann-Whitney U test. Variables that reached statistical significance in the univariate analysis were considered for the multivariate model based on multivariate binary logistic regression. mRMR, RF, and LASSO were implemented using ‘mRMRe’, ‘randomForest’, and ‘glmnt’, respectively. The Delong test was used to measure the differences in the ROC curves [25]. P < 0.05 was considered statistically significant.

Patient characteristics

A total of 224 patients with HCC (n = 149, 124 men and 25 women; mean age, 56.8 ± 11.9 years) and FNH (n = 75, 30 men and 45 women; mean age, 37.0 ± 12.1 years) were enrolled in this study. The clinical factors of FNHs and HCCs in the training and validation sets are shown in Table 1. All clinical factors showed no significant difference between the training and validation sets (P > 0.05).

Table 1

Clinical factors of the training and validation sets
Clinical factors	Training set (n = 156)			Validation set (n = 68)
	HCC (n = 104)	FNH (n = 52)	P	HCC (n = 45)	FNH (n = 23)	P	P*
Gender, Male/Female	84/20	18 /34	0.001	40/5	12 /11	0.002	0.136
Age, ≤ 50 / >50 years	33/71	45/7	0.001	12/33	19/4	0.001	0.644
HbsAg, Negative/ Positive	21/83	38/14	0.001	16/29	16/7	0.016	0.252
AFP, ≤ 400 / > 400 ng/mL	99/5	52/0	0.261	45/0	23/0	-	0.317
MRI tumour number, Single/ Multiple	89/15	36/16	0.028	40/5	16/7	0.101	0.838
MRI tumour size, cm	4.35 [2.60, 6.25]	2.50 [1.90, 3.33]	0.001	4.40 [3.00, 6.20]	2.70 [2.05, 3.95]	0.006	0.849
Liver steatosis, Absent/ Present	78/26	39/13	1.000	33/12	20/3	0.331	0.762
Liver haemangioma, Absent/ Present	88/16	49/3	0.141	42/3	22/1	1.000	0.235
Location, Internal/ Subcapsular	36/68	30/22	0.010	16/29	11/12	0.474	0.829
Margin, Ill-defined/ Well-defined	26/78	11/41	0.739	11/34	3/20	0.434	0.734
Shape, Not round/ Round	46/58	29/23	0.234	18/27	16/7	0.040	0.905
Pseudocapsule, Absent/ Present	67/37	28/24	0.270	24/21	13/10	1.000	0.448
Lesion homogeneity, Heterogeneous/ Homogeneous	68/36	21/31	0.005	29/16	7/16	0.016	0.672
Lesion with steatosis, Absent/ Present	86/18	49/3	0.082	38/7	22/1	0.337	0.895
Central vascular supply, Absent/ Present	90/14	49/3	0.238	36/9	21/2	0.396	0.380
Central scar, Absent/ Present	82/22	37/15	0.387	40/5	14/9	0.017	0.734
Enhancement pattern			0.001			0.001	0.56
Early enhancement + washout	82	15	-	36	7	-	-
Early enhancement + no washout	14	35	-	7	16	-	-
Other patterns	8	2	-	2	0	-	-

Note: HCC, Hepatocellular carcinoma in noncirrhotic liver; FNH, focal nodular hyperplasia; HbsAg, Hepatitis B surface antigen; AFP, Alpha fetoprotein; P* represents the P value of comparison between training and validation set.

Inter- and intra- observer agreement

We extracted 2,260 radiomics features in each patient from the AP and PVP of the ceMRI. For intra-observer agreement, the agreement was 85.3% (mean ICC = 0.90). For inter-observer agreement, the rate of agreement for all 2,260 features reached 78.4% (mean ICC = 0.85). Seven hundred and thirty-one features were excluded.

Radiomics feature selection and model construction

After combining the top 20 engineered features ranked by the mRMR and RF algorithms, 33 features were identified from the training set, with seven features selected simultaneously by two algorithms (Fig. 3). A Pearson correlation analysis of the 33 features showed that 11 pairs of features were highly correlated with Pearson correlation coefficients above 0.80. Finally, 22 features were subjected to LASSO regression, and eight features were selected with the best tuned regularization parameter λ of 0.041 under the 1-SE criterion found by 10-fold cross validation. The selected features were calculated according to the following formula to build a radiomics model: Rad score = − 6.68 * (PVP-glcm-wavelet-HHL-InverseVariance) – 3.87 * (AP-firstorder-original-10Percentile) – 2.81 * (PVP-glcm-log-sigma-1-5-mm-3D-MaximumProbability) – 1.65 * (PVP-glcm-MaximumProbability) + 0.08 * (AP-glcm-log-sigma-1-0-mm-3D-ClusterShade) + 0.11 * (PVP-first order-wavelet-HLL-Median) + 0.54 * (AP-firstorder-log-sigma-0-5-mm-3D-Median) + 1.81 * (AP-shape-original-Elongation). The Mann-Whitney U test of the Rad score was performed in the training and validation sets, and statistically significant differences were found between the two sets (Fig. 4).

Construction of the clinical model and combined model

Univariate analysis showed that age, sex, HbsAg, MRI tumor size, MRI tumor number, location, lesion homogeneity, and enhancement pattern reached statistical significance (P < 0.05). Multivariate analysis showed that age (OR = 11.09 [3.13–49.40], P < 0.001), sex (OR = 5.57 [1.74–19.85], P = 0.005), HbsAg (OR = 14.75 [4.43–60.94], P < 0.001), and enhancement pattern (OR = 0.21 [0.07–0.52], P = 0.001) were independent predictors for differential diagnosis of HCC and FNH, and they were used to build the clinical model. A combined model was also built using the four clinical factors and Rad score by logistic regression.

Diagnostic performance of the three models

Good performance of the clinical model, radiomics model, and combined model for the training set was observed, with an area under the curve (AUC) of 0.937 (95% CI 0.887–0.970), 0.960 (95% CI 0.916–0.985), and 0.984 (95% CI 0.949–0.997), with a classification accuracy of 0.853, 0.917, and 0.956, respectively. When comparing the AUCs between the three models, the combined model proved to be significantly better than the clinical model (P = 0.002), but the difference between the clinical model and radiomics model was not statistically significant.

Consistent results were obtained in the validation set. The AUC of the clinical model, radiomics model, and combined model for the validation set was 0.903 (95% CI 0.807–0.962), 0.931 (95% CI 0.843–0.978), and 0.972 (95% CI 0.900–0.997), with a classification accuracy of 0.853, 0.868, and 0.941, respectively. When comparing the AUCs between the three models, the combined model proved to be significantly better than the clinical model (P = 0.032), but the difference between the clinical model and radiomics model was not statistically significant (Table 2).

Table 2

Model performance in the training and validation sets
Model	Cutoff	AUC(95%CI)	Specificity	Sensitivity	Accuracy	P
Training set
Clinical Model	0.684	0.937(0.887–0.970)	0.923	0.817	0.853	Ref
Radiomics Model	0.695	0.960(0.916–0.985)	0.942	0.904	0.917	0.252
Combined Model	0.607	0.984(0.949–0.997)	0.962	0.952	0.956	0.002
Validation set
Clinical Model	0.625	0.903(0.807–0.962)	0.826	0.867	0.853	Ref
Radiomics Model	0.658	0.931(0.843–0.978)	0.826	0.889	0.868	0.535
Combined Model	0.859	0.972(0.900-0.997)	0.957	0.933	0.941	0.032

Note: AUC, area under the curve; CI, Confidence interval.

In this study, we established three models to distinguish HCC from FNH in non-cirrhotic liver using four clinical factors and a Rad score, which was combined with eight radiomics features filtrated from arterial phase and portal venous phase on MRI. In comparison to the clinical model, the combined model showed overall superiority in the evaluation of accuracy, sensitivity, specificity, and AUC in both the training and validation sets (Table 2, Fig. 5). The addition of radiomics features improved the performance of the diagnostic model, but the radiomics model did not bring significant improvement compared to the clinical model.

Many previous studies have provided several ways to differentiate HCC from FNH. Li et al. [26] enrolled 38 patients with HCC and 65 with FNH to assess the diagnostic ability of contrast-enhanced US (ceUS) and microflow (MF) imaging and found that MF imaging had an excellent diagnostic performance in terms of differentiating between atypical HCC and FNH compared to routine ceUS. Yu et al. [27] included 42 HCCs and 16 FNHs and performed spectral CT during the arterial and portal venous phases, and found that CT spectral imaging increased the detectability and accuracy of differentiation between HCC and FNH. Nie et al. [28] developed and validated a CT-based radiomics nomogram for preoperative differentiation of FNH from HCC in livers without cirrhosis, achieving an AUC of 0.917 in the validation group. Several studies [29–31] have indicated that Gd-EOBDTPA-MRI is helpful for the diagnosis of FNH, as most FNHs show high- or iso- signal intensity (SI) compared to liver parenchyma in the hepatobiliary phase (HBP). However, Lee et al. [32] found that 85% of well-differentiated HCCs were hypointense on HBP, and about 15% of well-differentiated HCCs were iso- or hyperintense on HBP, illustrating that there is still some overlap between them, even in the HBP. In this study, we established a combined model for differential diagnosis of HCC from FNH in non-cirrhotic livers. Our model is non-invasive and easy to implement, and it achieved excellent performance with an AUC of 0.972 in the validation set.

In our study, the clinical model did not achieve the best AUC (0.937 and 0.903 in the training and validation sets, respectively), but it was still relatively high. Although we included as many of the radiological features that we could to help identify the two diseases as mentioned in the EASL Clinical Practice Guide for benign liver tumors, such as liver hemangioma, steatosis in lesions, and the liver, they turned out not to be strong predictors. Presence of a central scar is a typical feature of FNH, which is identified on MRI in approximately 30–50% of FNH cases [8]. On the other hand, about 50% of non-cirrhotic HCCs have a central scar that is detectable by MRI, especially in fibrolamellar carcinoma [33]. In our study, 32 % FNHs had the central scar, which was consistent with previous studies. Nevertheless, based on our results, we suggest that in the absence of available MRI, age, sex and HbsAg are the main reference indicators in making differential diagnosis between HCC and FNH in non-cirrhotic liver .

Our results were consistent with the study reported by Nie et al. [28] They also included only one radiological feature in their model, which was enhancement pattern, as we did. The epidemiological and clinical characteristics of these two diseases are also important references for differential diagnosis. FNH mainly occurs in females (up to 90% of cases), with an average age between 35 and 50 years. HCC mainly occurs in elderly males and is usually accompanied by hepatitis B virus infection. The three clinical factors (age, sex, and HbsAg) were consistent with the epidemiological differences between the two diseases, indicating the interpretability of our models.

Radiomics includes an enormous amount of data with high-dimensional characteristics, so it is important to know how to extract the key features from such a huge amount of data. In order to ensure the reproducibility of the selected features and avoid the interference by other subjective factors, we implemented rigorous feature selection in combination with machine learning. First, inter-observer and intra-observer agreements were evaluated, and features with an ICC > 0.8 were included. Second, two machine learning algorithms, mRMR and RF, were used for feature filtering. Third, a correlation analysis of the features screened by the two algorithms listed above was performed to exclude features of high correlation. Finally, LASSO regression, which is one of the most commonly used methods for dimensionality reduction in radiomics, was carried out to obtain the optimal radiomics signature.

Our study had several limitations. First, the number of samples was still limited compared to the large number of features. A large-scale clinical study enrolling more samples would help validate and improve the applicability of our model as an effective tool for differentiating between FNH and HCC. Second, external validation is needed to further verify the accuracy and clinical practicability of the model. Finally, sample selection bias was unavoidable in this retrospective study. Therefore, a prospective study should be conducted to further prove the practicability of the model.

In conclusion, our novel MR-based radiomics model demonstrated a powerful diagnostic capability because of its excellent performance, with a certain reference value for differentiating HCC from FNH in clinical studies.

Ethics approval and consent to participate

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due [We plan to use this data set to publish another paper in a different research area] but are available from the corresponding author on reasonable request.

Competing interests

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Financial support:

This study was supported by Science and Technology project of Fuzhou (Grant number: 2019-SZ-48)

Acknowledgements

We would like to thank Editage (www.editage.cn) for English language editing.

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Editorial decision: Major Revision
04 Apr, 2021
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Review #1 received at journal
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Reviewer #2 agreed at journal
23 Feb, 2021
Reviewer #1 agreed at journal
14 Feb, 2021
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You are reading this latest preprint version

An MR-Based Radiomics Model for Differentiation Between Hepatocellular Carcinoma and Focal Nodular Hyperplasia in Non-Cirrhotic Liver

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Version 1

Abstract

Figures

Introduction

Materials And Methods

Patients

MR Image acquisition and image processing

Inter-observer and intra-observer agreement

Radiomics feature selection and model construction

Construction of the clinical model and combined model

Statistics

Results

Patient characteristics

Inter- and intra- observer agreement

Radiomics feature selection and model construction

Construction of the clinical model and combined model

Diagnostic performance of the three models

Discussion

Declarations

References

Status:

Version 1