Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions.

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review.

To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists' performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists' workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.

Looking through the eyes of the multidisciplinary team: the design and clinical evaluation of a decision support system for lung cancer care.

BACKGROUND: Decision-making in lung cancer is complex due to a rapidly increasing amount of diagnostic data and treatment options. The need for timely and accurate diagnosis and delivery of care demands high-quality multidisciplinary team (MDT) collaboration and coordination. Clinical decision support systems (CDSSs) can potentially support MDTs in constructing a shared mental model of a patient case. This enables the team to assess the strength and completeness of collected diagnostic data, stratification for the right personalized therapy driven by clinical stage and other treatment-influencing factors, and adapt care management strategies when needed. Current CDSSs often have a suboptimal fit into the decision-making workflow, which hampers their impact in clinical practice. METHODS: A CDSS for multidisciplinary decision-making in lung cancer was designed to support the abovementioned goals through presentation of relevant clinical data in line with existing mental model structures of the MDT members. The CDSS was tested in a simulated multidisciplinary tumor board meeting for primary diagnosis and treatment selection, based on de-identified primary lung cancer cases (n=8). Decision course analysis, eye-tracking data and questionnaires were used to assess the impact of the CDSS on constructing shared mental models to improve the decision-making process and outcome. RESULTS: The CDSS supported the team in their self-correcting capacity for accurate diagnosis and TNM classification. It enabled cross-validation of diagnostic findings, surfaced discordance between diagnostic tests and facilitated cancer staging according the diagnostic evidence, as well as spotting contra-indications for personalized treatment selection. CONCLUSIONS: This study shows the potential of CDSS on clinical decision making, when these systems are properly designed in line with clinical thinking. The presented setup enables assessment of the impact of CDSS design on clinical decision making and optimization of CDSSs to maximize their effect on decision quality and confidence.

Immersive training: breaking the bubble and measuring the heat.

BACKGROUND: Minimal access surgery and, lately, single-incision laparoscopic procedures are challenging and demanding with regard to the skills of the surgeon performing the procedures. This article presents the results of an investigation of the performance and attention focus of 21 medical interns and surgical residents training in an immersive context. That is, training 'in situation', representing more realistically the demands imposed on the surgeons during minimal access surgery. METHODS: Twenty-one medical interns and surgical residents participated in simulation trainings in an integrated operating room for laparoscopic surgery. Various physiological measures of body heat expenditure were gathered as indicators of mental strain and attention focus. RESULTS: The results of the Mann-Whitney test indicated that participants with a poor performance in the two laparoscopic cholecystectomy cases had a significantly (U = 3, p = 0.038) higher heat flux at the start of the procedure (mean 107.08, standard deviation [SD] 24.34) than those who excelled in the two cases (mean 62.64, SD 23.41). Also, the average frontal head temperature of the participants who failed at the task was significantly lower (mean 33.27, SD 0.52) than those who performed well (mean 33.92, SD 0.27). CONCLUSIONS: Surgeons cannot operate in a bubble; thus, they should not be trained in one. Combining heat flux and frontal head temperature could be a good measure of deep involvement and attentional focus during performance of simulated surgical tasks.

Do absorption and realistic distraction influence performance of component task surgical procedure?

BACKGROUND: Surgeons perform complex tasks while exposed to multiple distracting sources that may increase stress in the operating room (e.g., music, conversation, and unadapted use of sophisticated technologies). This study aimed to examine whether such realistic social and technological distracting conditions may influence surgical performance. METHODS: Twelve medical interns performed a laparoscopic cholecystectomy task with the Xitact LC 3.0 virtual reality simulator under distracting conditions (exposure to music, conversation, and nonoptimal handling of the laparoscope) versus nondistracting conditions (control condition) as part of a 2 x 2 within-subject experimental design. RESULTS: Under distracting conditions, the medical interns showed a significant decline in task performance (overall task score, task errors, and operating time) and significantly increased levels of irritation toward both the assistant handling the laparoscope in a nonoptimal way and the sources of social distraction. Furthermore, individual differences in cognitive style (i.e., cognitive absorption and need for cognition) significantly influenced the levels of irritation experienced by the medical interns. CONCLUSION: The results suggest careful evaluation of the social and technological sources of distraction in the operation room to reduce irritation for the surgeon and provision of proper preclinical laparoscope navigation training to increase security for the patient.