Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers

Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [18F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [18F]F-FDG. The novel non-[18F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions' location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[18F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [18F]F-FDG.

Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

 Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

 The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

 AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

  · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs)

Nuclear medicine has acquired a crucial role in the management of patients with neuroendocrine neoplasms (NENs) by improving the accuracy of diagnosis and staging as well as their risk stratification and personalized therapies, including radioligand therapies (RLT). Artificial intelligence (AI) and radiomics can enable physicians to further improve the overall efficiency and accuracy of the use of these tools in both diagnostic and therapeutic settings by improving the prediction of the tumor grade, differential diagnosis from other malignancies, assessment of tumor behavior and aggressiveness, and prediction of treatment response. This systematic review aims to describe the state-of-the-art AI and radiomics applications in the molecular imaging of NENs.

Prognostic Implications of PET-Derived Tumor Volume and Uptake in Patients with Neuroendocrine Tumors

Historically, molecular imaging of somatostatin receptor (SSTR) expression in patients with neuroendocrine tumors (NET) was performed using SSTR scintigraphy (SRS). Sustained advances in medical imaging have led to its gradual replacement with SSTR positron-emission tomography (SSTR-PET). The higher sensitivity in comparison to SRS on the one hand and conventional cross-sectional imaging, on the other hand, enables more accurate staging and allows for image quantification. In addition, in recent years, a growing body of evidence has assessed the prognostic implications of SSTR-PET-derived prognostic biomarkers for NET patients, with the aim of risk stratification, outcome prognostication, and prediction of response to peptide receptor radionuclide therapy. In this narrative review, we give an overview of studies examining the prognostic value of advanced SSTR-PET-derived (semi-)quantitative metrics like tumor volume, uptake, and composite metrics. Complementing this analysis, a discussion of the current trends, clinical implications, and future directions is provided.

Clinical application of AI-based PET images in oncological patients

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

· Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

CITATION FORMAT

· Feuerecker B, Heimer M, Geyer T et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105 - 114.

Impact of SSTR PET on Inter-Observer Variability of Target Delineation of Meningioma and the Possibility of Using Threshold-Based Segmentations in Radiation Oncology

Aim: The aim of this study was to assess the effects of including somatostatin receptor agonist (SSTR) PET imaging in meningioma radiotherapy planning by means of changes in inter-observer variability (IOV). Further, the possibility of using threshold-based delineation approaches for semiautomatic tumor volume definition was assessed. Patients and Methods: Sixteen patients with meningioma undergoing fractionated radiotherapy were delineated by five radiation oncologists. IOV was calculated by comparing each delineation to a consensus delineation, based on the simultaneous truth and performance level estimation (STAPLE) algorithm. The consensus delineation was used to adapt a threshold-based delineation, based on a maximization of the mean Dice coefficient. To test the threshold-based approach, seven patients with SSTR-positive meningioma were additionally evaluated as a validation group. Results: The average Dice coefficients for delineations based on MRI alone was 0.84 ± 0.12. For delineation based on MRI + PET, a significantly higher dice coefficient of 0.87 ± 0.08 was found (p < 0.001). The Hausdorff distance decreased from 10.96 ± 11.98 mm to 8.83 ± 12.21 mm (p < 0.001) when adding PET for the lesion delineation. The best threshold value for a threshold-based delineation was found to be 14.0% of the SUVmax, with an average Dice coefficient of 0.50 ± 0.19 compared to the consensus delineation. In the validation cohort, a Dice coefficient of 0.56 ± 0.29 and a Hausdorff coefficient of 27.15 ± 21.54 mm were found for the threshold-based approach. Conclusions: SSTR-PET added to standard imaging with CT and MRI reduces the IOV in radiotherapy planning for patients with meningioma. When using a threshold-based approach for PET-based delineation of meningioma, a relatively low threshold of 14.0% of the SUVmax was found to provide the best agreement with a consensus delineation.

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

Radiolabeled Somatostatin Analogues for Diagnosis and Treatment of Neuroendocrine Tumors

Simple Summary

Neuroendocrine neoplasms (NENs) are rare and heterogeneous tumors, presenting in often challenging clinical scenarios, and require multidisciplinary discussion for optimal care. The theranostic approach (DOTA peptides labelled with ⁶⁸Ga for imaging well-differentiated neuroendocrine tumors NETs, and labelled with ⁹⁰Y or ¹⁷⁷Lu for therapy) plays a crucial role in the management of NENs to assess disease extension and criteria for peptide receptor radionuclide therapy (PRRT) eligibility of based on somatostatin receptor (SSTR) expression. The present paper is an overview of currently employed radiolabeled SSTR analogues used for both diagnosis and therapy of NENs. Further emerging radiopharmaceuticals targeting SSTRs (e.g., fluorinated SSTR agonists, radiolabeled SSTR antagonists) as well as strategies to improve PRRT efficacy (by means of implementation of personalized treatment schemes, dosimetry, amelioration of response assessment strategies, and optimization of treatment sequencing) are also discussed. Finally, although very preliminary, some studies employing radiomic features in various kinds of NET are reported.

Abstract

Neuroendocrine neoplasms (NENs) are rare and heterogeneous tumors that require multidisciplinary discussion for optimal care. The theranostic approach (DOTA peptides labelled with ⁶⁸Ga for diagnosis and with ⁹⁰Y or ¹⁷⁷Lu for therapy) plays a crucial role in the management of NENs to assess disease extension and as a criteria for peptide receptor radionuclide therapy (PRRT) eligibility based on somatostatin receptor (SSTR) expression. On the diagnostic side, [⁶⁸Ga]Ga-DOTA peptides PET/CT (SSTR PET/CT) is the gold standard for imaging well-differentiated SSTR-expressing neuroendocrine tumors (NETs). [¹⁸F]FDG PET/CT is useful in higher grade NENs (NET G2 with Ki-67 > 10% and NET G3; NEC) for more accurate disease characterization and prognostication. Promising emerging radiopharmaceuticals include somatostatin analogues labelled with ¹⁸F (to overcome the limits imposed by ⁶⁸Ga), and SSTR antagonists (for both diagnosis and therapy). On the therapeutic side, the evidence gathered over the past two decades indicates that PRRT is to be considered as an effective and safe treatment option for SSTR-expressing NETs, and is currently included in the therapeutic algorithms of the main scientific societies. The positioning of PRRT in the treatment sequence, as well as treatment personalization (e.g., tailored dosimetry, re-treatment, selection criteria, and combination with other alternative treatment options), is warranted in order to improve its efficacy while reducing toxicity. Although very preliminary (being mostly hampered by lack of methodological standardization, especially regarding feature selection/extraction) and often including small patient cohorts, radiomic studies in NETs are also presented. To date, the implementation of radiomics in clinical practice is still unclear. The purpose of this review is to offer an overview of radiolabeled SSTR analogues for theranostic use in NENs.

Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics

PURPOSE

In the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.

METHODS

A total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.

RESULTS

A total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.

CONCLUSIONS

A prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.

Prognostic value of whole-body PET volumetric parameters extracted from 68Ga-DOTATOC-PET/CT in well-differentiated neuroendocrine tumors

Aim: To evaluate the prognostic value of somatostatin receptor tumor burden (SRTB) at ⁶⁸Ga-DOTATOC positron emission tomography/computed tomography (PET/CT) in patients with well-differentiated neuroendocrine tumors (WD-NETs). Methods: We retrospectively analyzed ⁶⁸Ga-DOTATOC-PET/CT of 84 patients with histologically confirmed WD-NETs (51 G1, 30 G2 and 3 G3). For each PET/CT, all DOTATOC-avid lesions were independently segmented by 2 operators using a customized threshold based on the healthy liver maximum standardized uptake value (SUV_max) using LIFEx 5.1. Somatostatin receptor expressing tumor volume (SRETV) and total lesion somatostatin receptor expression (TLSRE=SRETV*SUVmean) were extracted for each lesion and then whole-body SRETV and TLSRE (SRETVwb and TLSREwb) were defined as the sum of SRETV and TLSRE of all segmented lesions in each patient, respectively. Time to progression (TTP) was defined as the combination of disease-free-survival in patients undergoing curative surgery (n = 10) and progression-free survival for patients with unresectable/metastatic disease (n = 74). TTP and overall survival (OS) were calculated by Kaplan-Meier analysis, log-rank test, and Cox's proportional hazard model. Results: After a median follow-up period of 15.5 months disease progression was confirmed in 35 patients (41.7%) and 14 patients died. Higher SRETVwb (>39.1ml) and TLSREwb (>306.8g) were significantly correlated with shorter median TTP (TTP = 12months vs not reached; p<0.001). In multivariate analysis, SRETVwb (P = 0.005) was the only independent predictor of TTP regardless of histopathologic grade and TNM staging. Conclusion: According to our results, SRETVwb and TLSREwb extracted from ⁶⁸Ga-DOTATOC-PET/CT could predict TTP/OS and might have an important clinical utility in the management of in patients with WD-NETs.

Radiomics-Based Texture Analysis of 68Ga-DOTATATE Positron Emission Tomography and Computed Tomography Images as a Prognostic Biomarker in Adults With Neuroendocrine Cancers Treated With 177Lu-DOTATATE

Purpose

Neuroendocrine tumors (NET) are rare cancers with variable behavior. A better understanding of prognosis would aid individualized management. The aim of this hypothesis-generating pilot study was to investigate the prognostic potential of tumor heterogeneity and tracer avidity in NET using texture analysis (TA) of ⁶⁸Ga-DOTATATE positron emission tomography (PET) and non-enhanced computed tomography (CT) performed at baseline in patients treated with ¹⁷⁷Lu-DOTATATE. It aims to justify a larger-scale study to evaluate its clinical value.

Methods

The pretherapy ⁶⁸Ga-DOTATATE PET-CT scans of 44 patients with metastatic NET (carcinoid, pancreatic, thyroid, head and neck, catecholamine-secreting, and unknown primary NET) treated with ¹⁷⁷Lu-DOTATATE were analyzed retrospectively using commercially available texture analysis research software. Image filtration extracted and enhanced objects of different sizes (fine, medium, coarse), then quantified heterogeneity by statistical and histogram-based parameters (mean intensity, standard deviation, entropy, mean of positive pixels, skewness, and kurtosis). Regions of interest were manually drawn around up to five of the most ⁶⁸Ga-DOTATATE avid lesions for each patient. ⁶⁸Gallium uptake on PET was quantified as SUV_max and SUV_mean. Associations between imaging and clinical markers with progression-free (PFS) and overall survival (OS) were assessed using univariate Kaplan-Meier analysis. Independence of the significant univariate markers of survival was tested using multivariate Cox regression analysis.

Results

Measures of heterogeneity (higher kurtosis, higher entropy, and lower skewness) on coarse-texture scale CT and unfiltered PET images predicted shorter PFS (CT coarse kurtosis: p=0.05, PET entropy: p=0.01, PET skewness: p=0.03) and shorter OS (CT coarse kurtosis: p=0.05, PET entropy: p=0.01, PET skewness p=0.02). Conventional PET parameters such as SUV_max and SUV_mean showed trends towards predicting outcome but were not statistically significant. Multivariate analysis identified that CT-TA (coarse kurtosis: HR=2.57, 95% CI=1.22–5.38, p=0.013) independently predicted PFS, and PET-TA (unfiltered skewness: HR=9.05, 95% CI=1.19–68.91, p=0.033) independently predicted OS.

Conclusion

These preliminary data generate a hypothesis that radiomic analysis of neuroendocrine cancer on ⁶⁸Ga-DOTATATE PET-CT may be of prognostic value and a valuable addition to the assessment of patients.

A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods

OBJECTIVE

This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline ¹⁸F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology.

METHODS

The initial staging ¹⁸F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann-Whitney U test) was used for each feature, in pairwise comparisons of independent variables. A p value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with p < 0.05. Correlation between the significant features was evaluated with Spearman correlation test; features with correlation coefficient < 0.8 were evaluated with several ML algorithms.

RESULTS

In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination.

CONCLUSION

Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.