Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

Artificial intelligence innovations in neurosurgical oncology: a narrative review

PURPOSE

Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes.

METHODS

A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors.

RESULTS

Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens.

CONCLUSION

While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.

Synergy between psychological impact and biochemical manifestation of stress among the COVID-19 pandemic-affected population

Objective

Due to the COVID-19 pandemic, many countries around the world experienced an unprecedented increase in stress in the general population. Even after normal life has been reestablished, the new normal is punctuated by severely impacted vulnerable groups. Stress-associated symptoms display an intricate relationship with biochemical modulations, which coordinate the stress response. Identifying these biochemical factors is inherent to deciphering the mode of treatment needed to diminish the health-care gap resulting from the pandemic.

Methods

We applied psychological measures using the perceived stress (PS) and COVID-19 anxiety (CA) scales and preventive health behavior (PHB) to evaluate stress in the general population. Biochemical markers of stress, that is, total antioxidant capacity (TAC), thiobarbituric acid reactive substances (TBARS), cortisol, and C-reactive protein (CRP) were tested in the serum samples of the participants. Statistical analysis was carried out using SPSS version 22.0.

Results

Stress scores for PS, CA, and PHB indicate the prevalence of moderate-to-high stress among participants, and a correlation between psychological stress and biochemical correlates, TAC, TBARS, cortisol, and CRP. Serum concentrations of TBARS, Cortisol, and CRP were found to be significantly increased, while the TAC was decreased across all stress types and levels. Our findings demonstrate a positive correlation between PS, CA, PHB TBARS, cortisol, and CRP and a strong negative correlation with TAC.

Conclusion

The results of this study will help in tailoring targeted interventions and preventive regimes to mitigate COVID-19-associated anxiety and stress disorders prevailing even after the actual pandemic has subsided.

Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing

BACKGROUND

Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents.

METHODS

This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation.

RESULTS

Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome.

CONCLUSION

These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.

Supportive care and healthcare service utilisation in older adults with a new cancer diagnosis: a population-based review

OBJECTIVES

Older adults have unique needs and may benefit from additional supportive services through their cancer journey. It can be challenging for older adults to navigate the siloed systems within cancer centres and the community. We aimed to document the use of supportive care services in older adults with a new cancer diagnosis in a public healthcare system.

METHODS

We used population-based databases in British Columbia to document referrals to supportive care services. Patients aged 70 years and above with a new diagnosis of solid tumour in the year 2015 were included. Supportive care services captured were social work, psychiatry, palliative care, nutrition and home care. Chart review was used to assess visits to the emergency room and extra calls to the cancer centre help line.

RESULTS

2014 patients were included with a median age of 77, 30% had advanced cancer. 459 (22.8%) of patients accessed one or more services through the cancer centre. The most common service used was patient and family counselling (13%). 309 (15.3%) of patients used community home care services. Patients aged 80 years and above were less likely to access supportive care resources (OR 0.57) compared with those 70-79 years. Patients with advanced cancer, those treated at smaller cancer centres, and patients with colorectal, gynaecological and lung cancer were more likely to have received a supportive care referral.

CONCLUSIONS

Older adults, particularly those above 80 years, have low rates of supportive care service utilisation. Barriers to access must be explored, in addition to novel ways of holistic care delivery.