Texture in the monitoring of regorafenib therapy in patients with colorectal liver metastases

Radiomics and liver: Where we are and where we are headed?

Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.

Radiomics and Machine Learning for prediction of two-year disease-specific mortality and KRAS mutation status in metastatic colorectal cancer

PURPOSE

Colorectal cancer is usually accompanied by liver metastases. The prediction of patient evolution is essential for the choice of the appropriate therapy. The aim of this study is to develop and evaluate machine learning models to predict KRAS gene mutations and 2-year disease-specific mortality from medical images.

METHODS

Clinical and follow-up information was collected from patients with metastatic colorectal cancer who had undergone computed tomography prior to liver resection. The dominant liver lesion was segmented in each scan and radiomic features were extracted from the volumes of interest. The 65% of the cases were employed to perform feature selection and to train machine learning algorithms through cross-validation. The best performing models were assembled and evaluated in the remaining cases of the cohort.

RESULTS

For the mortality model development, 101 cases were used as training set (64 alive, 37 deceased) and 35 as test set (22 alive, 13 deceased); while for KRAS mutation models, 55 cases were used for training (31 wild-type, 24 mutated) and 30 for testing (17 wild-type, 13 mutated). The ensemble of top performing models resulted in an area under the receiver operating characteristic curve of 0.878 for mortality and 0.905 for KRAS prediction.

CONCLUSIONS

Predicting the prognosis of patients with metastatic colorectal cancer is useful for making timely decisions about the best treatment options. This study presents a noninvasive method based on quantitative analysis of baseline images to identify factors influencing patient outcomes, with the aim of incorporating these tools as support systems.

Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning

OBJECTIVES

To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs.

METHODS

Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model.

RESULTS

Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively.

CONCLUSIONS

A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs.

CLINICAL RELEVANCE STATEMENT

A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time.

KEY POINTS

• A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.

Focal Lesions of the Liver and Radiomics: What Do We Know?

Despite differences in pathological analysis, focal liver lesions are not always distinguishable in contrast-enhanced magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CT), and positron emission tomography (PET). This issue can cause problems of differential diagnosis, treatment, and follow-up, especially in patients affected by HBV/HCV chronic liver disease or fatty liver disease. Radiomics is an innovative imaging approach that extracts and analyzes non-visible quantitative imaging features, supporting the radiologist in the most challenging differential diagnosis when the best-known methods are not conclusive. The purpose of this review is to evaluate the most significant CT and MRI texture features, which can discriminate between the main benign and malignant focal liver lesions and can be helpful to predict the response to pharmacological or surgical therapy and the patient's prognosis.

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

BACKGROUND

Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance.

METHODS

We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73^High vs. CD73^Low) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set.

RESULTS

TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73^High vs rad-CD73^Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020).

CONCLUSIONS

Our findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.

Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible

Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors' detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker.

Radiomics in colorectal cancer patients

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

Radiomics in Breast Imaging: Future Development

Breast cancer is the most common and most commonly diagnosed non-skin cancer in women. There are several risk factors related to habits and heredity, and screening is essential to reduce the incidence of mortality. Thanks to screening and increased awareness among women, most breast cancers are diagnosed at an early stage, increasing the chances of cure and survival. Regular screening is essential. Mammography is currently the gold standard for breast cancer diagnosis. In mammography, we can encounter problems with the sensitivity of the instrument; in fact, in the case of a high density of glands, the ability to detect small masses is reduced. In fact, in some cases, the lesion may not be particularly evident, it may be hidden, and it is possible to incur false negatives as partial details that may escape the radiologist's eye. The problem is, therefore, substantial, and it makes sense to look for techniques that can increase the quality of diagnosis. In recent years, innovative techniques based on artificial intelligence have been used in this regard, which are able to see where the human eye cannot reach. In this paper, we can see the application of radiomics in mammography.

A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques

An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.

Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases

PURPOSE

The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query METHODS: The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures.

RESULTS

The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model.

CONCLUSIONS

Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.

Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern

To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.

Radiomics in medical imaging: pitfalls and challenges in clinical management

BACKGROUND

Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye.

METHODS

This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed.

RESULTS

Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting.

CONCLUSIONS

Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.

Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases

PURPOSE

To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients.

METHODS

This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered.

RESULTS

The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%.

CONCLUSIONS

Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases

Simple Summary

The objective of the study was to assess the radiomic features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with colorectal liver metastases, in order to predict histopathological outcomes following liver resection. We obtained good performance considering the single significant textural metric in the identification of the front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type, and in the detection of recurrences.

Abstract

Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

An update on radiomics techniques in primary liver cancers

BACKGROUND

Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting.

METHODS

This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed.

RESULTS

In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring.

CONCLUSIONS

Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model.

TRIAL REGISTRATION

Not applicable.

EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases

Simple Summary

The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. Ours results confirmed the capacity of radiomics to identify, as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. These results were confirmed by external validation dataset. We obtained a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences.

Abstract

The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study

Simple Summary

The objective of the study was to evaluate the radiomics features obtained by contrast MRI studies as prognostic biomarkers in colorectal liver metastases patients to predict clinical outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Moreover, considering linear regression models or neural network classifiers in a multivariate approach was possible to increase the performance in terms of accuracy, sensitivity, and specificity.

Abstract

Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis was approved by the local Ethical Committee board, and radiological databases were used to select patients with colorectal liver metastases with pathological proof and MRI study in a pre-surgical setting after neoadjuvant chemotherapy. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest on MRI by two expert radiologists, 851 radiomics features were extracted as median values using the PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The best predictor to discriminate expansive versus infiltrative tumor growth front was wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis extracted on portal phase with accuracy of 82%, sensitivity of 84%, and specificity of 77%. The best predictor to discriminate tumor budding was wavelet_LLH_firstorder_10Percentile extracted on portal phase with accuracy of 92%, a sensitivity of 96%, and a specificity of 81%. The best predictor to differentiate the mucinous type of tumor was the wavelet_LLL_glcm_ClusterTendency extracted on portal phase with accuracy of 88%, a sensitivity of 38%, and a specificity of 100%. The best predictor to identify the recurrence was the wavelet_HLH_ngtdm_Complexity extracted on arterial phase with accuracy of 90%, a sensitivity of 71%, and a specificity of 95%. The best linear regression model was obtained in the identification of mucinous type considering the 13 textural significant metrics extracted by arterial phase (accuracy of 94%, sensitivity of 77% and a specificity of 99%). The best results were obtained in the identification of tumor budding with the eleven textural significant features extracted by arterial phase using a KNN (accuracy of 95%, sensitivity of 84%, and a specificity of 99%). Conclusions: Our results confirmed the capacity of radiomics to identify as biomarkers and several prognostic features that could affect the treatment choice in patients with liver metastases in order to obtain a more personalized approach.

Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.

Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis

Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.

Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients

Simple Summary

Colorectal cancer (CRC) is the third leading cause of cancer and the second most deadly tumor type in the world. The liver is the most common site of metastasis in CRC patients. The conversion of new imaging biomarkers into diagnostic, prognostic and predictive signatures, by the development of radiomics and radiogenomics, is an important potential new tool for the clinical management of cancer patients. In this review, we assess the knowledge gained from radiomics and radiogenomics studies in liver metastatic colorectal cancer patients and their possible use for early diagnosis, response assessment and treatment decisions.

Abstract

Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data that can be mined for new biomarkers that show the biology of pathological processes at microscopic levels. These data can be converted into image-based signatures to improve diagnostic, prognostic and predictive accuracy in cancer patients. The combination of radiomics and molecular data, called radiogenomics, has clear implications for cancer patients’ management. Though some studies have focused on radiogenomics signatures in hepatocellular carcinoma patients, only a few have examined colorectal cancer metastatic lesions in the liver. Moreover, the need to differentiate between liver lesions is fundamental for accurate diagnosis and treatment. In this review, we summarize the knowledge gained from radiomics and radiogenomics studies in hepatic metastatic colorectal cancer patients and their use in early diagnosis, response assessment and treatment decisions. We also investigate their value as possible prognostic biomarkers. In addition, the great potential of image mining to provide a comprehensive view of liver niche formation is examined thoroughly. Finally, new challenges and current limitations for the early detection of the liver premetastatic niche, based on radiomics and radiogenomics, are also discussed.

Advanced image analytics predicting clinical outcomes in patients with colorectal liver metastases: A systematic review of the literature

BACKGROUND

To better select patients with colorectal liver metastases (CRLM) for an optimal selection of treatment strategy (i.e. local, systemic or combined treatment) new prognostic models are warranted. In the last decade, radiomics has emerged as a field to create predictive models based on imaging features. This systematic review aims to investigate the current state and potential of radiomics to predict clinical outcomes in patients with CRLM.

METHODS

A comprehensive literature search was conducted in the electronic databases of PubMed, Embase, and Cochrane Library, according to PRISMA guidelines. Original studies reporting on radiomics predicting clinical outcome in patients diagnosed with CRLM were included. Clinical outcomes were defined as response to systemic treatment, recurrence of disease, and survival (overall, progression-free, disease-free). Primary outcome was the predictive performance of radiomics. A narrative synthesis of the results was made. Methodological quality was assessed using the radiomics quality score.

RESULTS

In 11 out of 14 included studies, radiomics was predictive for response to treatment, recurrence of disease, survival, or a combination of outcomes. Combining clinical parameters and radiomic features in multivariate modelling often improved the predictive performance. Different types of individual features were found prognostic. Noticeable were the contrary levels of heterogeneous and homogeneous features in patients with good response. The methodological quality as assessed by the radiomics quality score varied considerably between studies.

CONCLUSION

Radiomics appears a promising non-invasive method to predict clinical outcome and improve personalized decision-making in patients with CRLM. However, results were contradictory and difficult to compare. Standardized prospective studies are warranted to establish the added value of radiomics in patients with CRLM.

CT based radiomic approach on first line pembrolizumab in lung cancer

Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment.

Primary Gastro-Intestinal Lymphoma and Gastro-Intestinal Adenocarcinoma: An Initial Study of CT Texture Analysis as Quantitative Biomarkers for Differentiation

BACKGROUND

To explore the potential role of computed tomography (CT) texture analysis and an imaging biomarker in differentiating primary gastro-intestinal lymphoma (PGIL) from gastro-intestinal adenocarcinoma (GIAC).

METHODS

A total of 131 patients with surgical pathologically PGIL and GIAC were enrolled in this study. Histogram parameters of arterial and venous phases extracted from contrast enhanced modified discrete cosine transform (MDCT) images were compared between PGIL and GIAC by Mann-Whitney U tests. The optimal parameters for differentiating these two groups were obtained through receiver operating characteristic (ROC) curves and the area under the curve (AUC) was calculated.

RESULTS

Compared with GIAC, in arterial phase, PGIL had statistically higher 5th, 10th percentiles (p = 0.003 and 0.011) and statistically lower entropy (p = 0.001). In the venous phase, PGIL had statistically lower mean, median, 75th, 90th, 95th percentiles, and entropy (p = 0.036, 0.029, 0.007, 0.001 and 0.001, respectively). For differentiating PGIL from GIAC, V-median + A-5th percentile was an optimal parameter for combined diagnosis (AUC = 0.746, p < 0.0001), and the corresponding sensitivity and specificity were 81.7 and 64.8%, respectively.

CONCLUSION

CT texture analysis could be useful for differential diagnosis of PGIL and GIAC.

Radiomics of Liver Metastases: A Systematic Review

Simple Summary

Patients with liver metastases can be scheduled for different therapies (e.g., chemotherapy, surgery, radiotherapy, and ablation). The choice of the most appropriate treatment should rely on adequate understanding of tumor biology and prediction of survival, but reliable biomarkers are lacking. Radiomics is an innovative approach to medical imaging: it identifies invisible-to-the-human-eye radiological patterns that can predict tumor aggressiveness and patients outcome. We reviewed the available literature to elucidate the role of radiomics in patients with liver metastases. Thirty-two papers were analyzed, mostly (56%) concerning metastases from colorectal cancer. Even if available studies are still preliminary, radiomics provided effective prediction of response to chemotherapy and of survival, allowing more accurate and earlier prediction than standard predictors. Entropy and homogeneity were the radiomic features with the strongest clinical impact. In the next few years, radiomics is expected to give a consistent contribution to the precision medicine approach to patients with liver metastases.

Abstract

Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.

Computed Tomography-Based Tumor Heterogeneity Analysis Reveals Differences in a Cohort with Advanced Pancreatic Carcinoma under Palliative Chemotherapy

Purpose

Imaging in pancreatic cancer is a challenge, especially regarding therapy response evaluation. Tumor size, attenuation, and perfusion are widely used as parameters for computed tomography (CT) examinations, but are often limited due to blurry tumor borders and missing qualitative parameters. To improve monitoring of therapy response, we tested a new CT-based approach of tumor heterogeneity feature analysis.

Methods

A total of 13 patients with pancreatic adenocarcinoma undergoing abdominal CT according to standard as baseline imaging with clinical follow-up and imaging (median time span 64 days) under systematic therapy (FOLFIRINOX/gemcitabine) were retrospectively analyzed. Progression was defined as new lesions and local tumor spread. Tumor heterogeneity analysis was performed using mintLesion®. Seven different image features referring to image heterogeneity were analyzed. Statistical analysis was performed with Spearman's rank correlation and Mann-Whitney U test.

Results

During follow-up, tumor volume did not significantly change between our groups with overall progression (local and systemic) and progression-free patients (p = 0.661). Mean positivity of pixel values were significantly higher in patients without progression compared to patients with progression (p = 0.030). There was a significant negative correlation between changes in kurtosis and time to local tumor spread (p = 0.008) or systemic progression (p = 0.017).

Conclusions

Results suggest that analysis of tumor heterogeneity might provide valuable information from routine-acquired images regarding therapy response evaluation. This might help adjusting therapy regimes and could be easily integrated in clinical workflows. Furthermore, this procedure might possibly predict therapy response and, hence could lead the way to find a potential marker for progression-free survival.