Machine Learning Algorithm Guiding Local Treatment Decisions to Reduce Pain for Lung Cancer Patients with Bone Metastases, a Prospective Cohort Study

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

BACKGROUND/OBJECTIVES

Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

METHODS

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

RESULTS

Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).

CONCLUSION

Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Evaluation of an automated matching system of children and families to virtual mental health resources during COVID-19

BACKGROUND

Children and their families often face obstacles in accessing mental health (MH) services. The purpose of this study was to develop and pilot test an electronic matching process to match children with virtual MH resources and increase access to treatment for children and their families during COVID-19.

METHODS

Within a large observational child cohort, a random sample of 292 families with children ages 6-12 years were invited to participate. Latent profile analysis indicated five MH profiles using parent-reported symptom scores from validated depression, anxiety, hyperactivity, and inattention measures: (1) Average Symptoms, (2) Low Symptoms, (3) High Symptoms, (4) Internalizing, and (5) Externalizing. Children were matched with virtual MH resources according to their profile; parents received surveys at Time 1 (matching process explanation), Time 2 (match delivery) and Time 3 (resource uptake). Data on demographics, parent MH history, and process interest were collected.

RESULTS

128/292 families (44%) completed surveys at Time 1, 80/128 families (63%) at Time 2, and a final 67/80 families (84%) at Time 3, yielding an overall uptake of 67/292 (23%). Families of European-descent and those with children assigned to the Low Symptoms profile were most likely to express interest in the process. No other factors were associated with continued interest or uptake of the electronic matching process. Most participating parents were satisfied with the process.

CONCLUSIONS

The electronic matching process delivered virtual MH resources to families in a time-efficient manner. Further research examining the effectiveness of electronically matched resources in improving children's MH symptoms is needed.

Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review

The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behavior information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Background/objective

Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.

Methods

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.

Results

This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).

Conclusion

Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer

BACKGROUND

Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables.

METHODS

Retrospective complete-case analysis of oesophagectomy patients ± neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) between 2010 and 2020. Established ML algorithms (Multinomial Logistic regression (MLR), Random Forests (RF), Extreme Gradient Boosting (XGB)) and Decision Tree (DT) were used to train models predicting OC MDT treatment decisions: surgery (S), NACT + S or NACRT + S. Performance metrics included Area Under the Curve (AUC), Accuracy, Kappa, LogLoss, F1 and Precision -Recall AUC. Variable importance was calculated for each model.

RESULTS

We identified 399 cases with a male-to-female ratio of 3.6:1 and median age of 66.1yrs (range 32-83). MLR outperformed RF, XGB and DT across performance metrics (mean AUC of 0.793 [±0.045] vs 0.757 [±0.068], 0.740 [±0.042], and 0.709 [±0.021] respectively). Variable importance analysis identified age as a major factor in the decision to offer surgery alone or NACT + S across models (p < 0.05).

CONCLUSIONS

ML techniques can use limited feature-sets to predict curative UGI MDT treatment decisions. Explainable Artificial Intelligence methods provide insight into decision-critical variables, highlighting underlying subconscious biases in cancer care decision-making. Such models may allow prioritization of caseload, improve efficiency, and offer data-driven decision-assistance to MDTs in the future.

The Oesophageal Cancer Multidisciplinary Team: Can Machine Learning Assist Decision-Making?

BACKGROUND

The complexity of the upper gastrointestinal (UGI) multidisciplinary team (MDT) is continually growing, leading to rising clinician workload, time pressures, and demands. This increases heterogeneity or 'noise' within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In recent decades, the application of artificial intelligence (AI) and more specifically the branch of machine learning (ML) has led to a paradigm shift in the perceived utility of statistical modelling within healthcare. Within oesophageal cancer (OC) care, ML techniques have already been applied with early success to the analyses of histological samples and radiology imaging; however, it has not yet been applied to the MDT itself where such models are likely to benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy.

METHODS

This review discusses the current role the MDT plays in modern UGI cancer care as well as the utilisation of ML techniques to date using histological and radiological data to predict treatment response, prognostication, nodal disease evaluation, and even resectability within OC.

RESULTS

The review finds that an emerging body of evidence is growing in support of ML tools within multiple domains relevant to decision-making within OC including automated histological analysis and radiomics. However, to date, no specific application has been directed to the MDT itself which routinely assimilates this information.

CONCLUSIONS

The authors feel the UGI MDT offers an information-rich, diverse array of data from which ML offers the potential to standardise, automate, and produce more consistent, data-driven MDT decisions.

The development of machine learning in lung surgery: A narrative review

Background

Machine learning reflects an artificial intelligence that allows applications to improve their accuracy to predict outcomes, eliminating the need to conduct explicit programming on them. The medical field has increased its focus on establishing tools for integrating machine learning algorithms in laboratory and clinical settings. Despite their importance, their incorporation is minimal in the medical sector yet. The primary goal of this study is to review the development of machine learning in the field of thoracic surgery, especially lung surgery.

Methods

This article used the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA). The sources used to gather data are the PubMed, Cochrane, and CINAHL databases and the Google Scholar search engine.

Results

The study included 19 articles, where ten concentrated on the application of machine learning in especially lung surgery, six focused on the benefits and limitations of machine learning algorithms in lung surgery, and three provided an overview of the future of machine learning in lung surgery.

Conclusion

The outcome of this study indicates that the field of lung surgery has attempted to integrate machine learning algorithms. However, the implementation rate is low, owing to the newness of the concept and the various challenges it encompasses. Also, this study reveals the absence of sufficient literature discussing the application of machine learning in lung surgery. The necessity for future research on the topic area remains evident.

Investigation on the incidence and risk factors of lung cancer among Chinese hospital employees

Objective

In recent years, the lung cancer incidence has grown and the population is younger. We intend to find out the true detection rate of pulmonary nodules and the incidence of lung cancer in the population and search for the risk factors.

Method

Hospital employees ≥40 years old who underwent low‐dose computed tomography (CT) lung cancer screening from January 2019 to March 2022 were selected to record CT‐imaging characteristics, pathology, staging, and questionnaires to investigate past history, smoking history, diet, mental health, etc. PM2.5 and radiation intake in radiation‐related occupation received monitoring in hospital.

Result

The detection rate of suspicious pulmonary nodules was 9.1% (233/2552), and the incidence rate of lung cancer (including adenocarcinoma in situ) was 4.0% (103/2552). Morbidity among doctors, nurses, technicians, administers, and logistics was no difference (p = 0.184), but higher in women than in men (4.7% vs 2.4% p = 0.002). The invasiveness increased with age and CT density of nodules (p = 0.018). The relationship between lung cancer morbidity and PM2.5 was not clear (p = 0.543); and no lung cancer has been found in employees related ionizing radiation.

Conclusion

The high screening rate has brought about a high incidence of lung cancer. At present, the risk factor analysis of lung cancer based on small samples cannot find the direct cause. Most of the ground glass opacity (GGO)s detected by LDCT screening are indolent, but there are also rapidly progressive lung cancer. A predictive model to identify active and indolent GGO is necessary.

Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

INTRODUCTION

Pain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain.

METHODS

A systematic review of the current literature was conducted using the PubMed database library.

RESULTS

Twenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed.

CONCLUSION

ML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain.