Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR

Artificial intelligence in interventional radiology: state of the art

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.

An Interventional Radiologist's Primer of Critical Appraisal of Artificial Intelligence Research

Recent advances in artificial intelligence (AI) are expected to cause a significant paradigm shift in all digital data-driven aspects of information gain, processing, and decision making in both clinical healthcare and medical research. The field of interventional radiology (IR) will be enmeshed in this innovation, yet the collective IR expertise in the field of AI remains rudimentary because of lack of training. This primer provides the clinical interventional radiologist with a simple guide for critically appraising AI research and products by identifying 12 fundamental items that should be considered: (a) need for AI technology to address the clinical problem, (b) type of applied Al algorithm, (c) data quality and degree of annotation, (d) reporting of accuracy, (e) applicability of standardized reporting, (f) reproducibility of methodology and data transparency, (g) algorithm validation, (h) interpretability, (i) concrete impact on IR, (j) pathway toward translation to clinical practice, (k) clinical benefit and cost-effectiveness, and (l) regulatory framework.

Machine-Learned Algorithms to Predict the Risk of Pneumothorax Requiring Chest Tube Placement after Lung Biopsy

PURPOSE

To develop a machine-learned algorithm to predict the risk of postlung biopsy pneumothorax requiring chest tube placement (CTP) to facilitate preprocedural decision making, optimize patient care, and improve resource allocation.

MATERIALS AND METHODS

This retrospective study collected clinical and imaging features of biopsy samples obtained from patients with lung nodule biopsy and included information from 59 procedures resulting in pneumothorax requiring CTP and randomly selected 67 procedures without CTP (convenience sample). The data were divided into 70 and 30 as training and testing sets, respectively. Conventional machine-learned binary classifiers were explored with preprocedural imaging and clinical data as input features and CTP as the output.

RESULTS

There was no single pathognomonic imaging or predictive clinical feature. For the independent test set under the high-specificity mode, a decision tree, logistic regression, and Naïve Bayes classifier achieved accuracies of identifying CTP at 0.79, 0.93, and 0.89 and area under receiver operating curves (AUROCs) of 0.68, 0.76, and 0.82, respectively. Under high-sensitivity mode, a decision tree, logistic regression, and Naïve Bayes achieved accuracies of identifying CTP of 0.60, 0.45, and 0.60 with AUROCs of 0.71, 0.81, and 0.82, respectively. High importance features included lesion character, chronic obstructive pulmonary disease, lesion depth, and age. A coarse decision tree requiring 4 inputs achieved comparable performance as other methods and previous machine learning prediction studies.

CONCLUSIONS

The results support the possibility of predicting pneumothorax requiring CTP after biopsy based on an automated decision support, reliant on readily available preprocedural information.

Prediction of Complication Risk in Computed Tomography-guided Thoracic Biopsy: A Prescription for Improving Procedure Safety

PURPOSE

Computed tomography-guided transthoracic biopsy (CTTB) is a minimally invasive procedure with a high diagnostic yield for a variety of thoracic diseases. We comprehensively assessed a large CTTB cohort to predict procedural and patient factors associated with the risk of complications.

MATERIALS AND METHODS

The medical record and computed tomography images of 1430 patients who underwent CTTB were reviewed individually to obtain clinical information and technical procedure factors. Statistical analyses included descriptive and summary statistics, univariate analysis with the Fisher test, and multivariate logistic regression.

RESULTS

The most common type of complication was pneumothorax (17.4%), followed by bleeding (5.9%). Only 26 patients (1.8%) developed a major complication. Lung lesions carried a higher risk of complications than nonlung lesions. For lung lesions, the nondependent position of the lesion, vertical needle approach, trespassing aerated lung, and involvement of a trainee increased the risk of complication, whereas the use of the coaxial technique was a protective factor. The time with the needle in the lung, the number of biopsy samples, and the distance crossing the aerated lung were identified as additional risk factors in multivariate analysis. For nonlung lesions, trespassing the pleural space was the single best predictor of complications. A logistic regression-based model achieved an area under the receiver operating characteristic curve of 0.975, 0.699, and 0.722 for the prediction of major, minor, and no complications, respectively.

CONCLUSIONS

Technical procedural factors that can be modified by the operator are highly predictive of the risk of complications in CTTB.

Predicting survival for hepatic arterial infusion chemotherapy of unresectable colorectal liver metastases: Radiomics analysis of pretreatment computed tomography

Objective

Hepatic arterial infusion chemotherapy (HAIC) is an effective treatment for advanced unresectable colorectal cancer liver metastases (CRLM). This study was conducted to predict the efficacy of HAIC in patients with unresectable CRLM by radiomics methods based on pretreatment computed tomography (CT) examinations and clinical data.

Materials and Methods

A total of 63 patients were included in this study (41 in the training group and 22 in the validation group). All these patients underwent CT examination before HAIC. During the follow-up period, CT scans and laboratory examinations were performed regularly. Eighty-five radiological features were extracted from the regions of interest (ROIs) of CT images using the PyRadiomics program. The t-test and correlation were applied to select features. These features were analyzed using LASSO-Cox regression, and a linear model was developed to predict overall survival (OS).

Results

After reducing features by t-test and correlation test, seven features remained. After LASSO-Cox cross-validation, four features remained at λ = 0.232. They were gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM), and the location of the primary tumor. The C-index was 0.758 in the training group and 0.743 in the test group. Nomograms predicting 1-, 2-, and 3-year survival were established.

Conclusion

Our study demonstrates that a radiomics approach based on pretreatment CT texture analysis has the ability to predict early the outcome of HAIC in patients with advanced unresectable colorectal cancer with a high degree of accuracy and feasibility.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Machine learning approaches for the prediction of postoperative complication risk in liver resection patients

Background

For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions.

Objective

The aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications.

Methods

The study subjects were liver cancer patients who had undergone liver resection. There were 175 individuals, and 13 variables were recorded. 70% of the data were used for the training set, and 30% for the test set. The performance of five machine learning models, logistic regression, decision trees-C5.0, decision trees-CART, support vector machines, and random forests, for predicting postoperative complication risk in liver resection patients were compared. The significant influencing factors were selected by combining results of multiple methods, based on which the prediction model of postoperative complications risk was created. The results were analyzed to give suggestions of how to reduce the risk of complications.

Results

Random Forest gave the best performance from the decision curves analysis. The decision tree-C5.0 algorithm had the best performance of the five machine learning algorithms if ACC and AUC were used as evaluation indicators, producing an area under the receiver operating characteristic curve value of 0.91 (95% CI 0.77–1), with an accuracy of 92.45% (95% CI 85–100%), the sensitivity of 87.5%, and specificity of 94.59%. The duration of operation, patient’s BMI, and length of incision were significant influencing factors of postoperative complication risk in liver resection patients.

Conclusions

To reduce the risk of complications, it appears to be important that the patient's BMI should be above 22.96 before the operation, and the duration of the operation should be minimized.

Challenges of Implementing Artificial Intelligence in Interventional Radiology

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.

Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study

Purpose

To investigate the potential of texture analysis and machine learning to predict treatment response to transarterial radioembolization (TARE) on pre-interventional cone-beam computed tomography (CBCT) images in patients with liver metastases.

Materials and Methods

In this IRB-approved retrospective single-center study 36 patients with a total of 104 liver metastases (56 % male, mean age 61.1 ± 13 years) underwent CBCT prior to TARE and follow-up imaging 6 months after therapy. Treatment response was evaluated according to RECIST version 1.1 and dichotomized into disease control (partial response/stable disease) versus disease progression (progressive disease). After target lesion segmentation, 104 radiomics features corresponding to seven different feature classes were extracted with the pyRadiomics package. After dimension reduction machine learning classifications were performed on a custom artificial neural network (ANN). Ten-fold cross validation on a previously unseen test data set was performed.

Results

The average administered cumulative activity from TARE was 1.6 Gbq (± 0.5 Gbq). At a mean follow-up of 5.9 ± 0.8 months disease control was achieved in 82 % of metastases. After dimension reduction, 15 of 104 (15 %) texture analysis features remained for further analysis. On a previously unseen set of liver metastases the Multilayer Perceptron ANN yielded a sensitivity of 94.2 %, specificity of 67.7 % and an area-under-the receiver operating characteristics curve of 0.85.

Conclusion

Our study indicates that texture analysis-based machine learning may has potential to predict treatment response to TARE using pre-treatment CBCT images of patients with liver metastases with high accuracy.

Percutaneous Transthoracic Lung Biopsy: Optimizing Yield and Mitigating Risk

ABSTRACT

Percutaneous computed tomography-guided transthoracic lung biopsy is an effective and minimally invasive procedure to achieve tissue diagnosis. Radiologists are key in appropriate referral for further workup, with percutaneous computed tomography-guided transthoracic lung biopsy performed by both thoracic and general interventionalists. Percutaneous computed tomography-guided transthoracic lung biopsy is increasingly performed for both diagnostic and research purposes, including molecular analysis. Multiple patient, lesion, and technique-related variables influence diagnostic accuracy and complication rates. A comprehensive understanding of these factors aids in procedure planning and may serve to maximize diagnostic yield while minimizing complications, even in the most challenging scenarios.

Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions

The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in non-oncologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, post-procedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current 'black box' of AI research and help bridge the gap between the research laboratory and clinical practice.

Circular suture of the uterine serosa and myometrium layer around placental attachment site for refractory postpartum hemorrhage

OBJECTIVE

The aim of the study was to analyze the clinical outcomes of circular suture at placental attachment site for refractory postpartum hemorrhage (PPH), which could block blood supply of the serosa and myometrium layer.

METHODS

Eighty cases of refractory PPH were enrolled and retrospective analyzed in this study for further analysis from a consecutive single center database between 2010 and 2018. After undergoing circular suture of the uterine serosa and myometrium layer around placental attachment site, surgical and perioperative outcomes were recorded and analyzed.

RESULTS

Among all the patients enrolled, 28 cases (35.0%) of refractory PPH were mainly caused by uterine inertia, 36 cases (45.0%) caused by ectopic placenta, and 2 cases (2.5%) caused by coagulation disorders. After circular suture of the uterine serosa and myometrium layer at placental attachment site, all the uterine active bleeding was controlled below 40 ml without recurrence. The perioperative results were similar between the vaginal and cesarean sections groups.

CONCLUSIONS

Circular suture of the uterine serosa and myometrium at the placental attachment site could control refractory PPH with few postoperative complications. Circular suture around placenta site could be applied in time to protect the endometrium even in primary hospital.