Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement

Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma

Purpose

To predict distant metastasis (DM) in patients with borderline resectable pancreatic carcinoma using delta-radiomics features calculated from contrast-enhanced computed tomography (CECT) and non-CECT images.

Methods and Materials

Among 250 patients who underwent radiation therapy at our institution between February 2013 and December 2021, 67 patients were deemed eligible. A total of 11 clinical features and 3906 radiomics features were incorporated. Radiomics features were extracted from CECT and non-CECT images, and the differences between these features were calculated, resulting in delta-radiomics features. The patients were randomly divided into the training (70%) and test (30%) data sets for model development and validation. Predictive models were developed with clinical features (clinical model), radiomics features (radiomics model), and a combination of the abovementioned features (hybrid model) using Fine-Gray regression (FG) and random survival forest (RSF). Optimal hyperparameters were determined using stratified 5-fold cross-validation. Subsequently, the developed models were applied to the remaining test data sets, and the patients were divided into high- or low-risk groups based on their risk scores. Prognostic power was assessed using the concordance index, with 95% CIs obtained through 2000 bootstrapping iterations. Statistical significance between the above groups was assessed using Gray's test.

Results

At a median follow-up period of 23.8 months, 47 (70.1%) patients developed DM. The concordance indices of the FG-based clinical, radiomics, and hybrid models were 0.548, 0.603, and 0.623, respectively, in the test data set, whereas those of the RSF-based models were 0.598, 0.680, and 0.727, respectively. The RSF-based model, including delta-radiomics features, significantly divided the cumulative incidence curves into two risk groups (P < .05). The feature map of the gray-level size-zone matrix showed that the difference in feature values between CECT and non-CECT images correlated with the incidence of DM.

Conclusions

Delta-radiomics features obtained from CECT and non-CECT images using RSF successfully predict the incidence of DM in patients with borderline resectable pancreatic carcinoma.

Radiomic features based on pyradiomics predict CD276 expression associated with breast cancer prognosis

Background

CD276 is a promising immune checkpoint molecule with significant therapeutic potential. Several clinical trials are currently investigating CD276-targeted therapies.

Purpose

This study aims to assess the prognostic significance of CD276 expression levels and to predict its expression using a radiomic approach in breast cancer (BC).

Methods

A cohort of 840 patients diagnosed with BC from The Cancer Genome Atlas was included in this study. The Cancer Imaging Archive provided 98 magnetic resonance imaging (MRI) scans, which were randomly allocated to training and validation datasets in a 7:3 ratio. The association between CD276 expression and patient survival was assessed using Cox regression analysis. Feature selection was performed using the maximum relevance minimum redundancy algorithm and recursive feature elimination. Subsequently, support vector machine (SVM) and logistic regression (LR) models were constructed to predict CD276 expression.

Results

The expression of CD276 was found to be elevated in BC. It was an independent risk factor for overall survival (hazard ratio = 1.579, 95 % CI: 1.054-2.366). There were eight radiomic features selected in total. In both the training and validation subsets, the SVM and LR models demonstrated favorable predictive abilities with AUC values of 0.744 and 0.740 for the SVM model and 0.742 and 0.735 for the LR model. These results indicate that the radiomic models efficiently differentiate the CD276 expression status.

Conclusions

CD276 expression levels can have an impact on cancer prognosis. The MRI-based radiomic signature described in this study can discriminate the CD276 expression status.

Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma

Objectives

This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies.

Methods

A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

Results

Among the 233 models constructed using arterial phase (AP), venous phase (VP), and AP+VP radiomics features, the model built by applying AP+VP radiomics features and a combination of Lasso+Logistic algorithm had the highest mean AUC. A clinical model was eventually constructed using CA199 and tumor size. The combined model consisted of AP+VP-Radscore and two clinical factors that showed the best diagnostic efficiency in the training (AUC = 0.920) and validation (AUC = 0.866) cohorts. Regarding preoperative diagnosis of LN metastasis, the calibration curve and DCA demonstrated that the combined model had a good consistency and greatest net benefit.

Conclusions

Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.

Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis

PURPOSE

Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis.

MATERIALS AND METHODS

Thirty-three rabbits were randomly divided into 27 carbon tetrachloride-induced liver fibrosis group and 6 control group. Spectral CT contrast-enhanced scan was performed in batches, and the liver fibrosis was staged according to the histopathological results. The portal venous phase spectral CT parameters [70 keV CT value, normalized iodine concentration (NIC), spectral HU curve slope (λHU)] were measured, and MaZda texture analysis was performed on 70 keV monochrome images. Three dimensionality reduction methods and four statistical methods in B11 module were used to perform discriminant analysis and calculate misclassified rate (MCR), and ten texture features under the lowest combination of MCR were statistically analyzed. Receiver operating characteristic curve (ROC) was used to calculate the diagnostic performance of spectral parameters and texture features for significant liver fibrosis. Finally, the binary logistic regression was used to further screen independent predictors and establish model.

RESULTS

A total of 23 experimental rabbits and 6 control rabbits were included, of which 16 had significant liver fibrosis. Three spectral CT parameters with significant liver fibrosis were significantly lower than those of non-significant liver fibrosis (p < 0.05), and the AUC ranged from 0.846 to 0.913. The combination analysis of mutual information (MI) and nonlinear discriminant analysis (NDA) had the lowest MCR, which with 0%. In the filtered texture features, four were statistically significant and AUC > 0.5, ranges from 0.764 to 0.875. The logistic regression model showed that Perc.90% and NIC could be used as independent predictors, the overall prediction accuracy of the model was 89.7% and the AUC was 0.976.

CONCLUSION

Spectral CT parameters and texture features have high diagnostic value for predicting significant liver fibrosis in rabbits, and the combination of the two can improve its diagnostic efficiency.

Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients

BACKGROUND

Nearly one fourth of patients with pancreatic ductal adenocarcinoma (PDAC) occur to liver metastasis after surgery, and liver metastasis is a risk factor for prognosis for those patients with surgery therapy. However, there is no effective way to predict liver metastasis post-operation.

METHOD

Clinical data and preoperative magnetic resonance imaging (MRI) of PDAC patients diagnosed between July 2010 and July 2020 were retrospectively collected from three hospital centers in China. The significant MRI radiomics features or clinicopathological characteristics were used to establish a model to predict liver metastasis in the development and validation cohort.

RESULTS

A total of 204 PDAC patients from three hospital centers were divided randomly (7:3) into development and validation cohort. Due to poor predictive value of clinical features, MRI radiomics model had similar receiver operating characteristics curve (ROC) value to clinical-radiomics combing model in development cohort (0.878 vs. 0.880, p = 0.897) but better ROC in validation dataset (0.815 vs. 0.732, p = 0.022). Radiomics model got a sensitivity of 0.872/0.750 and a specificity of 0.760/0.822 to predict liver metastasis in development and validation cohort, respectively. Among 54 patients randomly selected with post-operation specimens, fibrosis markers (α-smooth muscle actin) staining was shown to promote radiomics model with ROC value from 0.772 to 0.923 (p = 0.049) to predict liver metastasis.

CONCLUSION

This study developed and validated an MRI-based radiomics model and showed a good performance in predicting liver metastasis in resectable PDAC patients.

Radiomics model-based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade

OBJECTIVE

To develop diagnostic radiomic model-based algorithm for pancreatic ductal adenocarcinoma (PDAC) grade prediction.

METHODS

Ninety-one patients with histologically confirmed PDAC and preoperative CT were divided into subgroups based on tumor grade. Two histology-blinded radiologists independently segmented lesions for quantitative texture analysis in all contrast enhancement phases. The ratio of densities of PDAC and unchanged pancreatic tissue, and relative tumor enhancement (RTE) in arterial, portal venous, and delayed phases of the examination were calculated. Principal component analysis was used for multivariate predictor analysis. The selection of predictors in the binary logistic model was carried out in 2 stages: (1) using one-factor logistic models (selection criterion was p < 0.1); (2) using regularization (LASSO regression after standardization of variables). Predictors were included in proportional odds models without interactions.

RESULTS

There were significant differences in 4, 16, and 8 texture features out of 62 for the arterial, portal venous, and delayed phases of the study, respectively (p < 0.1). After selection, the final diagnostic model included such radiomics features as DISCRETIZED HU standard, DISCRETIZED HUQ3, GLCM Correlation, GLZLM LZLGE for the portal venous phase of the contrast enhancement, and CONVENTIONAL_HUQ3 for the delayed phase of CT study. On its basis, a diagnostic model was built, showing AUC for grade ≥ 2 of 0.75 and AUC for grade 3 of 0.66.

CONCLUSION

Radiomics features vary in PDAC of different grades and increase the accuracy of CT in preoperative diagnosis. We have developed a diagnostic model, including texture features, which can be used to predict the grade of PDAC.

KEY POINTS

• A diagnostic algorithm based on CT texture features for preoperative PDAC grade prediction was developed. • The assumption that the scanning protocol can influence the results of texture analysis was confirmed and assessed. • Our results show that tumor differentiation grade can be assessed with sufficient diagnostic accuracy using CT texture analysis presented in this study.

Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

Background

The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation.

Purpose

To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC.

Material and Methods

In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps—variance threshold, variance thresholding and least absolute shrinkage selection operator—were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values.

Results

Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists’ assessment (0.600–0.613 and 0.560–0.587 in the training and validation sets, respectively).

Conclusion

Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists’ assessment.

Modern Radiation Diagnostics and Intelligent Personalized Technologies in Hepatopancreatology

Timely instrumental diagnosis of diseases of the hepatopancreatoduodenal region, especially of an oncological nature, is the key to successful treatment, improving prognosis and improving the quality of life of patients. At the moment, the possibilities of radiation diagnostics make it possible to identify and evaluate the nature of the blood supply to the neoplasm, its prevalence, cellularity, and in the case of MRI studies with hepatospecific contrast agents, also evaluate the functional activity of liver cells. Nevertheless, the steady development of methods for treating cancer patients, in particular, chemotherapy, and a personalized approach to the choice of patient management tactics require a detailed assessment of the morphological types of certain neoplasms. The need for dynamic monitoring of the results of treatment, monitoring of accidentally detected, potentially malignant neoplasms, and the development of screening programs determine the steady increase in the number of CT and MR examinations performed annually in the world and in our country. These factors have led to the application of texture analysis or radiomics and machine learning algorithms. At the same time, such techniques as radiography, ultrasound, CT and MRI with extracellular and tissue-specific contrast enhancement, and MRI-DWI do not lose their significance. The ongoing research allows the Federal State Budgetary Institution National Medical Research Center of Surgery named after A.V. Vishnevsky of the Ministry of Health of Russia to implement the concept of preoperative non-invasive diagnosis and differential diagnosis of surgical and oncological diseases of the hepatopancreatoduodenal region and apply the knowledge gained in planning surgical treatment. Implementation of the problem of post-processor data processing of radiation diagnostics of surgical and oncological diseases of the hepatopancreatoduodenal region using radiomics and AI technologies is important and extremely relevant for modern medicine.

Pancreatic cancer, radiomics and artificial intelligence

Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision-making, enabling personalised management of advanced PDAC. Deep learning and convolutional neural networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonisation, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold-standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and artificial intelligence solutions to predict PDAC aggressiveness in a clinical setting.

Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.

Analysis of the value of enhanced CT combined with texture analysis in the differential diagnosis of pulmonary sclerosing pneumocytoma and atypical peripheral lung cancer: a feasibility study

BACKGROUND

As a rare benign lung tumour, pulmonary sclerosing pneumocytoma (PSP) is often misdiagnosed as atypical peripheral lung cancer (APLC) on routine imaging examinations. This study explored the value of enhanced CT combined with texture analysis to differentiate between PSP and APLC.

METHODS

Forty-eight patients with PSP and fifty patients with APLC were retrospectively enrolled. The CT image features of the two groups of lesions were analysed, and MaZda software was used to evaluate the texture of CT venous phase thin-layer images. Independent sample t-test, Mann-Whitney U tests or χ² tests were used to compare between groups. The intra-class correlation coefficient (ICC) was used to analyse the consistency of the selected texture parameters. Spearman correlation analysis was used to evaluate the differences in texture parameters between the two groups. Based on the statistically significant CT image features and CT texture parameters, the independent influencing factors between PSP and APLC were analysed by multivariate logistic regression. Extremely randomized trees (ERT) was used as the classifier to build models, and the models were evaluated by the five-fold cross-validation method.

RESULTS

Logistic regression analysis based on CT image features showed that calcification and arterial phase CT values were independent factors for distinguishing PSP from APLC. The results of logistic regression analysis based on CT texture parameters showed that WavEnHL_s-1 and Perc.01% were independent influencing factors to distinguish the two. Compared with the single-factor model (models A and B), the classification accuracy of the model based on image features combined with texture parameters was 0.84 ± 0.04, the AUC was 0.84 ± 0.03, and the sensitivity and specificity were 0.82 ± 0.13 and 0.87 ± 0.12, respectively.

CONCLUSION

Enhanced CT combined with texture analysis showed good diagnostic value for distinguishing PSP and APLC, which may contribute to clinical decision-making and prognosis evaluation.

Myocardial area at risk and salvage in reperfused acute MI measured by texture analysis of cardiac T2 mapping and its prediction value of functional recovery in the convalescent stage

OBJECTIVES

We sought to distinguish area at risk from salvage myocardial zone and to predict left ventricle functional recovery in the convalescent stage by Texture Analysis (TA) of T2-Mapping.

METHODS

One hundred and six patients diagnosed with AMI and treated with percutaneous coronary intervention (PCI) underwent acute cardiac magnetic resonance imaging (CMR) and 45 of whom had a subsequent CMR scan following recovery. Cine imaging, T2-Mapping, T2-weighted STIR imaging, and LGE imaging were performed. In the texture analysis, regions of interest (infarcted, salvageable, and remote) were drawn by two blinded, independent readers.

RESULTS

Seven independent texture features on T2-Mapping were selected: Perc.50%, S(2,2)InvDfMom, S(2.-2)AngScMom, S(4,0)Entropy, 45dgrLngREmph, 45dgr_Fraction and 135dr_GLevNonU. Among them, the average value of 135dr_GLevNonU in the infarct zone, AAR zone, and the remote zone was: 61.96±26.03, 31.811±18.933 and 99.839±26.231, respectively. Additionally, 135dr_GLevNonU provided the highest area under the curve (AUC) from the receiver operating characteristic curve (ROC curve) for distinguishing AAR from the infarct zone in each subgroup (all patients, patients with MVO and)were 0.845 ± 0.052 0.855 ± 0.083 and 0.845 ± 0.066, respectively, and were more promise than T2-Mapping mean (p<0.001). The AUC for differentiating AAR from the remote zone is 0.942±0.041. Texture features are not associated with convalescent decreased strain, ejection fraction (EF) or left ventricle remodeling (LVR) in analysis (p>0.05).

CONCLUSION

TA of T2-mapping can distinguish AAR from both the infarct zone and the remote myocardial zone without LGE imaging in reperfused AMI. However, these features are not able to predict patients' functional recovery in the convalescent stage.

Pancreas image mining: a systematic review of radiomics

OBJECTIVES

To systematically review published studies on the use of radiomics of the pancreas.

METHODS

The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study.

RESULTS

A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003).

CONCLUSIONS

Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice.

KEY POINTS

• Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.

Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study

Background

To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC).

Method

One hundred four participants were enrolled in this retrospective study. Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture features were analyzed by MaZda, and B11 program was used for data analysis and classification. The diagnosis efficiencies of texture features and conventional imaging features in identifying the differentiated degree of HCC were assessed by receiver operating characteristic analysis. The relationship between texture features and differentiated degree of HCC was evaluated by Spearman’s correlation coefficient.

Results

The grey-level co-occurrence matrix -based texture features were most frequently extracted and the nonlinear discriminant analysis was excellent with the misclassification rate ranging from 3.33 to 14.93%. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively, while the AUC of tumor size was 0.649, 0.660 and 0.517 respectively. The tumor size was significantly different between poorly- and moderately-HCC (p = 0.014). The COMBINE AUC values were not increased with tumor size combined.

Conclusions

Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI was valuable and might be a promising method in identifying the differentiated degree of HCC. The poorly-differentiated HCC was more heterogeneous than well- and moderately-differentiated HCC.