Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma

Value of radiomics features extracted from baseline computed tomography images in predicting overall survival in patients with nonsurgical pancreatic ductal adenocarcinoma: incorporation of a radiomics score to a multiparametric nomogram to predict 1-year overall survival

PURPOSE

This study aimed to determine the value of radiomics features derived from baseline computed tomography (CT) scans and volumetric measurements to predict overall survival (OS) in patients with nonsurgical pancreatic ductal adenocarcinoma (PDAC) treated with a chemotherapy combination regimen of 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX).

METHODS

In this retrospective single-institution study, 131 patients with nonsurgical PDAC who received FOLFIRINOX neoadjuvant chemotherapy between December 2012 and November 2021 were included. Pretreatment contrast-enhanced CT images were obtained for all patients before inclusion. The primary tumor was contoured by an expert radiologist with 25 years of experience. A total of 845 radiomics features, including first-, second-, and higher-order features, were extracted from the total tumor volume. A feature reduction pipeline was used to reduce the dimensionality of the data. The selected features were used to generate a radiomics score based on the Least Absolute Shrinkage and Selection Operator coefficients. A high-dimensional Cox model was generated on the basis of the radiomics score and other quantitative and semantic imaging findings.

RESULTS

From the 845 radiomics features extracted, 45 were significantly different between the tertiles. The following equation was used to generate a radiomics score: radiomics score = SmallAreaEmphasis (-66.87801 + LargeDependenceEmphasis) - 0.2345916. The radiomics score was significantly different among the 3 groups of the radiomics features (P = .034). The overall difference in survival was significant among the 3 groups (P = .02). The nomogram showed good calibration and showed significant differences among the patients when they were classified as tertiles (P < .00).

CONCLUSION

Radiomics approaches have the potential to predict OS in nonsurgical patients with PDAC, and the inclusion of semantic imaging findings and pathologic data could further enhance prognostication in patients with PDAC.

Invasion in Advanced Gastric Cancer Based on Enhanced Computer Tomography Radiomics Nomogram

OBJECTIVE

To evaluate the efficacy of an enhanced computed tomography (CT) radiomics nomogram in predicting preoperative lymphovascular invasion (LVI) or perineural invasion (PNI) in patients with advanced gastric cancer (GC).

MATERIALS AND METHODS

Data from 149 patients with GC from our hospital (January 2019 to December 2022) were analyzed. High throughput radiomics features were extracted from manually delineated volumes of interest on enhanced CT venous phase images. Optimal features were identified using intraclass correlation coefficient analysis and least absolute shrinkage and selection operator. Models were constructed using the radiomics score (Rad-score), the above features, and independent risk factors. Performance was assessed via the receiver operating characteristic, decision curve analysis and calibration curves.

RESULTS

Eight radiomics features were deemed essential. Factors including history of alcohol consumption ( P = 0.029), peritumor fatty infiltration ( P = 0.046), degree of enhancement ( P = 0.012), and Rad-score ( P < 0.001) were significant predictors of LVI/PNI. The radiomics nomogram, which integrated these factors, showed superior prediction (the training group: area under the curve [AUC] = 0.917; the validation group: AUC = 0.925) compared with other models.

CONCLUSION

The enhanced CT radiomics nomogram offers robust preoperative prediction for LVI/PNI in patients with GC.

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.

Artificial Intelligence in Pancreatic Image Analysis: A Review

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.

Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care

Aim

The objective of this study is to develop accurate machine learning (ML) models for predicting the neurological status at hospital discharge of critically ill patients with hemorrhagic and ischemic stroke and identify the risk factors associated with the neurological outcome of stroke, thereby providing healthcare professionals with enhanced clinical decision-making guidance.

Materials and methods

Data of stroke patients were extracted from the eICU Collaborative Research Database (eICU-CRD) for training and testing sets and the Medical Information Mart for Intensive Care IV (MIMIC IV) database for external validation. Four machine learning models, namely gradient boosting classifier (GBC), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), were used for prediction of neurological outcome. Furthermore, shapley additive explanations (SHAP) algorithm was applied to explain models visually.

Results

A total of 1,216 hemorrhagic stroke patients and 954 ischemic stroke patients from eICU-CRD and 921 hemorrhagic stroke patients 902 ischemic stroke patients from MIMIC IV were included in this study. In the hemorrhagic stroke cohort, the LR model achieved the highest area under curve (AUC) of 0.887 in the test cohort, while in the ischemic stroke cohort, the RF model demonstrated the best performance with an AUC of 0.867 in the test cohort. Further analysis of risk factors was conducted using SHAP analysis and the results of this study were converted into an online prediction tool.

Conclusion

ML models are reliable tools for predicting hemorrhagic and ischemic stroke neurological outcome and have the potential to improve critical care of stroke patients. The summarized risk factors obtained from SHAP enable a more nuanced understanding of the reasoning behind prediction outcomes and the optimization of the treatment strategy.

Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images

Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.

Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN)

Intraductal papillary mucinous neoplasms (IPMNs) are a very common incidental finding during patient radiological assessment. These lesions may progress from low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and even pancreatic cancer. The IPMN progression risk grows with time, so discontinuation of surveillance is not recommended. It is very important to identify imaging features that suggest LGD of IPMNs, and thus, distinguish lesions that only require careful surveillance from those that need surgical resection. It is important to know the management guidelines and especially the indications for surgery, to be able to point out in the report the findings that suggest malignant degeneration. The imaging tools employed for diagnosis and risk assessment are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) with contrast medium. According to the latest European guidelines, MRI is the method of choice for the diagnosis and follow-up of patients with IPMN since this tool has a highest sensitivity in detecting mural nodules and intra-cystic septa. It plays a key role in the diagnosis of worrisome features and high-risk stigmata, which are associated with IPMNs malignant degeneration. Nowadays, the main limit of diagnostic tools is the ability to identify the precursor of pancreatic cancer. In this context, increasing attention is being given to artificial intelligence (AI) and radiomics analysis. However, these tools remain in an exploratory phase, considering the limitations of currently published studies. Key limits include noncompliance with AI best practices, radiomics workflow standardization, and clear reporting of study methodology, including segmentation and data balancing. In the radiological report it is useful to note the type of IPMN so as the morphological features, size, rate growth, wall, septa and mural nodules, on which the indications for surveillance and surgery are based. These features should be reported so as the surveillance time should be suggested according to guidelines.