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.

Role of multisensory stimuli in vigilance enhancement- a single trial event related potential study.

Development of interventions to prevent vigilance decrement has important applications in sensitive areas like transportation and defence. The objective of this work is to use multisensory (visual and haptic) stimuli for cognitive enhancement during mundane tasks. Two different epoch intervals representing sensory perception and motor response were analysed using minimum variance distortionless response (MVDR) based single trial ERP estimation to understand the performance dependency on both factors. Bereitschaftspotential (BP) latency L3 (r=0.6 in phase 1 (visual) and r=0.71 in phase 2 (visual and haptic)) was significantly correlated with reaction time as compared to that of sensory ERP latency L2 (r=0.1 in both phase 1 and phase 2). This implies that low performance in monotonous tasks is predominantly dependent on the prolonged neural interaction with the muscles to initiate movement. Further, negative relationship was found between the ERP latencies related to sensory perception and Bereitschaftspotential (BP) and occurrence of epochs when multisensory cues are provided. This means that vigilance decrement is reduced with the help of multisensory stimulus presentation in prolonged monotonous tasks.

Measuring vigilance decrement using computer vision assisted eye tracking in dynamic naturalistic environments.

Eye tracking offers a practical solution for monitoring cognitive performance in real world tasks. However, eye tracking in dynamic environments is difficult due to high spatial and temporal variation of stimuli, needing further and thorough investigation. In this paper, we study the possibility of developing a novel computer vision assisted eye tracking analysis by using fixations. Eye movement data is obtained from a long duration naturalistic driving experiment. Source invariant feature transform (SIFT) algorithm was implemented using VLFeat toolbox to identify multiple areas of interest (AOIs). A new measure called `fixation score' was defined to understand the dynamics of fixation position between the target AOI and the non target AOIs. Fixation score is maximum when the subjects focus on the target AOI and diminishes when they gaze at the non-target AOIs. Statistically significant negative correlation was found between fixation score and reaction time data (r =-0.2253 and p<;0.05). This implies that with vigilance decrement, the fixation score decreases due to visual attention shifting away from the target objects resulting in an increase in the reaction time.

EEG and Eye Tracking Demonstrate Vigilance Enhancement with Challenge Integration.

Maintaining vigilance is possibly the first requirement for surveillance tasks where personnel are faced with monotonous yet intensive monitoring tasks. Decrement in vigilance in such situations could result in dangerous consequences such as accidents, loss of life and system failure. In this paper, we investigate the possibility to enhance vigilance or sustained attention using "challenge integration," a strategy that integrates a primary task with challenging stimuli. A primary surveillance task (identifying an intruder in a simulated factory environment) and a challenge stimulus (periods of rain obscuring the surveillance scene) were employed to test the changes in vigilance levels. The effect of integrating challenging events (resulting from artificially simulated rain) into the task were compared to the initial monotonous phase. EEG and eye tracking data is collected and analyzed for n = 12 subjects. Frontal midline theta power and frontal theta to parietal alpha power ratio which are used as measures of engagement and attention allocation show an increase due to challenge integration (p < 0.05 in each case). Relative delta band power of EEG also shows statistically significant suppression on the frontoparietal and occipital cortices due to challenge integration (p < 0.05). Saccade amplitude, saccade velocity and blink rate obtained from eye tracking data exhibit statistically significant changes during the challenge phase of the experiment (p < 0.05 in each case). From the correlation analysis between the statistically significant measures of eye tracking and EEG, we infer that saccade amplitude and saccade velocity decrease with vigilance decrement along with frontal midline theta and frontal theta to parietal alpha ratio. Conversely, blink rate and relative delta power increase with vigilance decrement. However, these measures exhibit a reverse trend when challenge stimulus appears in the task suggesting vigilance enhancement. Moreover, the mean reaction time is lower for the challenge integrated phase (RTmean = 3.65 ± 1.4s) compared to initial monotonous phase without challenge (RTmean = 4.6 ± 2.7s). Our work shows that vigilance level, as assessed by response of these vital signs, is enhanced by challenge integration.

Eye tracking and EEG synchronization to analyze microsaccades during a workload task.

Electroencephalography (EEG) and eye tracking are two fields that have evolved independently to study topics such as mental workload, attention and fatigue in cognitive neuroscience. However, little research has been devoted to integrating data from these two fields. In this paper, we investigate the utility of a specific type of eye movement, microsaccades, to analyze cognitive activity. We assess mental workload using event related potentials (ERPs) correlated with microsaccades during experiments where task complexity is designed to be greater with an increase in visual degradation. We also develop a modified eye movement algorithm to identify microsaccades during tasks that permit regular saccades and blinks. We compare ERPs at microsaccade onset locked epochs to those of stimulus onset locked epochs. Our results show a clear correlation of ERP activations to both latency and activation areas. These findings provide important insights for analyzing sophisticated tasks in a non-invasive fashion.

Cognitive workload estimation due to vague visual stimuli using saccadic eye movements.

Visual perception is affected by the quality of stimulus. In this paper, we investigate the rise in cognitive workload of an individual performing visual task due to vague visual stimuli. We make use of normalized average peak saccadic velocity to estimate the cognitive workload. Results obtained from 16 human subjects show that the mean of peak saccadic velocity increases with workload indicating that faster saccades are required to obtain information as the workload increases. This technique should find application in assessment of vigilance and cognitive performance in many demanding professional, industrial and transportation situation.