► Glutamine is consumed at high rates by many cancer and proliferating cells in order to support bioenergetics. ► Glutamine also contributes carbon and nitrogen to biosynthetic processes and can ...impact signal transduction. ► A growing number of oncogenes and tumor suppressors have been shown to regulate glutamine uptake and metabolism. ► Metabolism of glucose and glutamine, is coordinated during proliferation. ► New technologies for imaging glutamine metabolism may improve diagnosis and monitoring of glutaminolytic tumors.
Increased glutaminolysis is now recognized as a key feature of the metabolic profile of cancer cells, along with increased aerobic glycolysis (the Warburg effect). In this review, we discuss the roles of glutamine in contributing to the core metabolism of proliferating cells by supporting energy production and biosynthesis. We address how oncogenes and tumor suppressors regulate glutamine metabolism and how cells coordinate glucose and glutamine as nutrient sources. Finally, we highlight the novel therapeutic and imaging applications that are emerging as a result of our improved understanding of the role of glutamine metabolism in cancer.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In recent decades, endovascular aneurysm repair or endovascular aortic repair (EVAR) has become an acceptable alternative to open surgery for the treatment of thoracic and abdominal aortic aneurysms ...and other aortic pathologies such as the acute aortic syndromes (e.g., penetrating aortic ulcer, intramural hematoma, dissection). Available data suggest that endovascular repair is associated with lower perioperative 30-day all-cause mortality as well as a significant reduction in perioperative morbidity when compared to open surgery. Additionally, EVAR leads to decreased blood loss, eliminates the need for cross-clamping the aorta and has shorter recovery periods than traditional surgery. It is currently the preferred mode of treatment of thoracic and abdominal aortic aneurysms in a subset of patients who meet certain anatomic criteria conducive to endovascular repair. The main disadvantage of EVAR procedures is the high rate of post-procedural complications that often require secondary re-intervention. As a result, most authorities recommend lifelong imaging surveillance following repair. Available surveillance modalities include conventional radiography, computed tomography, magnetic resonance angiography, ultrasonography, nuclear imaging and conventional angiography, with computed tomography currently considered to be the gold standard for surveillance by most experts. Following endovascular abdominal aortic aneurysm (AAA) repair, the rate of complications is estimated to range between 16% and 30%. The complication rate is higher following thoracic EVAR (TEVAR) and is estimated to be as high as 38%. Common complications include both those related to the endograft device and systemic complications. Device-related complications include endoleaks, endograft migration or collapse, kinking and/or stenosis of an endograft limb and graft infection. Post-procedural systemic complications include end-organ ischemia, cerebrovascular and cardiovascular events and post-implantation syndrome. Secondary re-interventions are required in approximately 19% to 24% of cases following endovascular abdominal and thoracic aortic aneurysm repair respectively. Typically, most secondary reinterventions involve the use of percutaneous techniques such as placement of cuff extension devices, additional endograft components or stents, enhancement of endograft fixation, treatment of certain endoleaks using various embolization techniques and embolic agents and thrombolysis of occluded endograft components. Less commonly, surgical conversion and/or open surgical modification are required. In this article, we provide an overview of the most common complications that may occur following endovascular repair of thoracic and AAAs. We also summarize the current surveillance recommendations for detecting and evaluating these complications and discuss various current secondary re-intervention approaches that may typically be employed for treatment.
Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In ...recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.
Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in ...improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71–0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67–0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objectives
Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of ...intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer.
Methods
In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest–based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance.
Results
Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival (
p
< 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94.
Conclusions
MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer.
Key Points
• MRI-based tumor heterogeneity texture features are associated with patient survival outcomes.
• MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer.
• Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in ...improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71-0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67-0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Purpose of review
The purpose of this review is to highlight the current role of machine learning and artificial intelligence and in the field of interventional oncology.
Recent findings
With ...advancements in technology, there is a significant amount of research regarding the application of artificial intelligence and machine learning in medicine. Interventional oncology is a field that can benefit greatly from this research through enhanced image analysis and intraprocedural guidance. These software developments can increase detection of cancers through routine screening and improve diagnostic accuracy in classifying tumors. They may also aid in selecting the most effective treatment for the patient by predicting outcomes based on a combination of both clinical and radiologic factors. Furthermore, machine learning and artificial intelligence can advance intraprocedural guidance for the interventional oncologist through more accurate needle tracking and image fusion technology. This minimizes damage to nearby healthy tissue and maximizes treatment of the tumor. While there are several exciting developments, this review also discusses limitations before incorporating machine learning and artificial intelligence in the field of interventional oncology. These include data capture and processing, lack of transparency among developers, validating models, integrating workflow, and ethical challenged.
Summary
In summary, machine learning and artificial intelligence have the potential to positively impact interventional oncologists and how they provide cancer care treatments.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, ...manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.
The aim of the present study was to evaluate the performance of whole-body diffusion-weighted imaging (WB-DWI), whole-body positron emission tomography with computed tomography (WB-PET/CT), and ...whole-body positron emission tomography with magnetic resonance imaging (WB-PET/MRI) in staging patients with untreated invasive ductal carcinoma of the breast. Fifty-one women with newly diagnosed invasive ductal carcinoma of the breast underwent WB-DWI, WB-PET/CT and WB-PET/MRI before treatment. A radiologist and a nuclear medicine physician reviewed in consensus the images from the three modalities and searched for occurrence, number and location of metastases. Final staging, according to each technique, was compared. Pathology and imaging follow-up were used as the reference. WB-DWI, WB-PET/CT and WB-PET/MRI correctly and concordantly staged 33/51 patients: stage IIA in 7 patients, stage IIB in 8 patients, stage IIIC in 4 patients and stage IV in 14 patients. WB-DWI, WB-PET/CT and WB-PET/MRI incorrectly and concordantly staged 1/51 patient as stage IV instead of IIIA. Discordant staging was reported in 17/51 patients. WB-PET/MRI resulted in improved staging when compared to WB-PET/CT (50 correctly staged on WB-PET/MRI vs. 38 correctly staged on WB-PET/CT; McNemar's test; p<0.01). Comparing the performance of WB-PET/MRI and WB-DWI (43 correct) did not reveal a statistically significant difference (McNemar test, p=0.14). WB-PET/MRI is more accurate in the initial staging of breast cancer than WB-DWI and WB-PET/CT, however, the discrepancies between WB-PET/MRI and WB-DWI were not statistically significant. When available, WB-PET/MRI should be considered for staging patient with invasive ductal breast carcinoma.
Purpose
Severe peripheral artery disease (PAD) may result in lower extremity amputation or require multiple procedures to achieve limb salvage. Current prediction models for major amputation risk ...have had limited performance at the individual level. We developed an interpretable machine learning model that will allow clinicians to identify patients at risk of amputation and optimize treatment decisions for PAD patients.
Methods
We utilized the American College of Surgeons National Surgical Quality Improvement Program database to collect preoperative clinical and laboratory information on 14,444 patients who underwent lower extremity endovascular procedures for PAD from 2011 to 2018. Using data from 2011 to 2017 for training and data from 2018 for testing, we developed a machine learning model to predict 30 day amputation in this patient population. We present performance metrics overall and stratified by race, sex, and age. We also demonstrate model interpretability using Gini importance and SHapley Additive exPlanations.
Results
A random forest machine learning model achieved an area under the receiver-operator curve (AU-ROC) of 0.81. The most important features of the model were elective surgery designation, claudication, open wound/wound infection, white blood cell count, and albumin. The model performed equally well on white and non-white patients (Delong
p
-value = 0.189), males and females (Delong
p
-value = 0.572), and patients under age 65 and patients age 65 and older (Delong
p
-value = 0.704).
Conclusion
We present a machine learning model that predicts 30 day major amputation events in PAD patients undergoing lower extremity endovascular procedures. This model can optimize clinical decision-making for patients with PAD.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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