JCO Clinical Cancer Informatics最新文献

Purpose: Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and uncertainty in the interpretability of results. We aimed to design a perfusion model applicable to breast cancer SoC dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series with results stable to low temporal resolution imaging, comparable with published results using full-resolution DCE-MRI, and correlative with orthogonal imaging modalities indicative of biophysical markers.

Methods: Subsampled high-temporal-resolution DCE-MRI series were run through our perfusion model and resulting fits were compared for consistency. The fits were also compared against previously published results from institutions using the full resolution series. The model was then evaluated on a separate cohort for validity of biomarker indications. Finally, the model was used as a fundamental part of predicting response to neoadjuvant chemotherapy (NACT).

Results: Temporally subsampled DCE-MRI series yield perfusion fit variations on the scale of 1% of the tumor median value when input frames are varied. Fits generated from pseudoclinical series are within the variation range seen between imaging sites (ρ = 0.55), voxel-wise. The model also demonstrates significant correlations with orthogonal positron emission tomography imaging, indicating potential for use as a biomarker proxy. Specifically, using the perfusion fits as the grounding for a biophysical simulation of response, we correctly predict the pathologic complete response status after NACT in 15 of 18 patients, for an accuracy of 0.83, with a specificity and sensitivity of 0.83 as well.

Conclusion: Clinical DCE-MRI data may be leveraged to provide stable perfusion fit results and indirectly interrogate the tumor microenvironment. These fits can then be used downstream for prediction of response to NACT with high accuracy.

Purpose: Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Registry (VCR), a population-based cancer registry, to assess (1) if VCR received all relevant pathology reports for three clinical trials, (2) AI accuracy in auto-extracting information from pathology reports for recruitment, and (3) the number of participants approached for trial enrollment using the AI approach compared with standard hospital-based recruitment.

Methods: To verify pathology report accessibility for VCR trial enrollment, reports from the laboratory were cross-referenced. To determine the accuracy of a Rapid Case Ascertainment (RCA) module of the AI software in extracting key clinical variables from the pathology report, data were compared with manually reviewed reports. To examine the effectiveness of the AI recruitment approach, the number of patients approached for recruitment was compared with standard practice.

Results: Of the 195 reports provided by the pathology laboratory, 185 (94.9%) were received by VCR, 73 of 195 (37.4%) were eligible for the studies, and 5 of 73 (6.8%) eligible cases had not been received by the VCR. The RCA module demonstrated an accuracy of 93% and an F1 score of 0.94 in extracting key clinical variables. However, the RCA false-positive rate was 10% and the false-negative rate was 5%. The standard hospital approach selected fewer cases for approach to clinical trials compared with the RCA module approach, 8 of 336 (2.4%) versus 12 of 336 (3.6%), respectively.

Conclusion: Using AI to screen potentially eligible cases for recruitment to three clinical trials resulted in a 50% increase in eligible cases being approached for enrollment.

Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.

Materials and methods: We analyzed 108 patients (n = 90 internal; n = 18 external) who received ablative radiotherapy. The internal data set included 42 patients with adrenal cancer, 23 patients with lung cancer, and 25 patients with pancreatic cancer, with the clinical end point of progression-free survival events. The median dose was 50 Gy, which was delivered over five fractions. The delta features are the ratio of the features of the last to first treatment fraction, F5/F1, and the concatenation of the first and last fraction features, F1||F5. Decision tree classifier with and without auxiliary data sets, and the external data set was used exclusively for independent testing of the final models.

Results: During internal training, for the F1||F5 model, the inclusion of the lung data set increased our AUC receiver operator characteristic curve (ROC) from 0.53 ± 0.12 to 0.61 ± 0.11, whereas the pancreatic data set increased our AUC-ROC to 0.60 ± 0.14. For the F5/F1 model, the inclusion of the lung auxiliary data increased our AUC-ROC from 0.52 ± 0.13 to 0.65 ± 0.11, whereas it modestly changed by 0.62 ± 0.13 with the pancreas. During external testing, for the F5/F1 model, we reported an AUC-ROC of 0.60 with the lung auxiliary data and 0.43 with the pancreatic data. Also, for the F5||F1 model, we reported an AUC-ROC of 0.70 with the lung auxiliary and 0.60 with the pancreatic data.

Conclusion: Decision trees provided an explainable model on the external data set. The validation of our model on an external data set may be the first step to biologically adapted radiotherapy recognizing radiomics signals for potential recurrence.

Purpose: Emoji are digital images or icons used to express an idea or emotion in electronic communication. The purpose of this study was to develop and evaluate the psychometric properties of two patient-reported scales that incorporate emoji.

Methods: The Emoji Response Scale developed for this study has two parts: the Emoji-Ordinal and Emoji-Mood scales. A pilot study was designed to validate the ordinal nature of the Emoji-Ordinal Scale. Twenty patients were shown all possible pairs of five emoji and asked to select the most positive from each pair. The psychometric ordering was assessed using Coombs unfolding and Thurstone scaling. A separate pilot study was designed to determine which emoji to include in the Emoji-Mood Scale. Ten common feelings experienced by patients with cancer were chosen by the study team. Patients and providers were asked to select the one emoji that best represented each feeling from the selection. The most commonly selected emotions and representative emoji were chosen for the Emoji-Mood Scale. In a randomized study of 294 patients, Spearman correlations, Wilcoxon tests, and Bland-Altman analyses determined the construct validity of the scales compared with Linear Analog Scale Assessments (LASA) and Patient-Reported Outcomes Measurement Information System (PROMIS) scores.

Results: Ninety-five percent of patients selected the same ordering among the ordinal emoji, and Thurstone scaling confirmed the ordinal nature of the response scale. The construct validity of the scales was high with correlations between the Emoji-Ordinal Scale and the LASA scale of 0.70 for emotional well-being, 0.72 for physical well-being, 0.74 for overall quality of life, and -0.81 for fatigue. Emoji-Mood Scale ratings were strongly related to PROMIS global mental, global physical, fatigue, anxiety, sleep disturbance, and social activity scales (P < .0001).

Conclusion: This study provides evidence that scales incorporating emoji are valid for collecting patient-reported outcomes.

Purpose: Modeling passively collected smartphone sensor data (called digital phenotyping) has the potential to detect distress among family caregivers and patients with advanced cancer and could lead to novel clinical models of cancer care. The purpose of this study was to assess the feasibility and acceptability of collecting passive smartphone data from family caregivers and their care recipients with advanced cancer over 24 weeks.

Methods: This was an observational feasibility study. Family caregivers and patients with advanced cancer were recruited through clinic or via social media and downloaded a digital phenotyping application (Beiwe) to their smartphones that passively collected sensor data over 24 weeks. Feasibility was evaluated by quantifying enrollment and retention and the quantity of acquired data. Acceptability was assessed through post-24 week qualitative interviews.

Results: Of 178 caregiver and patient dyads approached, 22.5% of caregivers (n = 40) and 10.1% of patients (n = 18) both consented to the study and successfully downloaded the application, with most recruited through social media (93%). Of 24 weeks (168 days), the median number of days that data were received was 141 days. Interviews yielded three themes: (1) experiences with study procedures were generally positive despite some technical challenges; (2) security and privacy concerns were minimal, mitigated by clear explanations, trust in the health care system, and privacy norms; and (3) a clinical model that used passive smartphone monitoring to automatically trigger assistance could be beneficial but with concern about false alarms.

Conclusion: This pilot study of collecting passive smartphone data found mixed indicators of feasibility, with suboptimal enrollment rates, particularly via clinic, but positive retention and data collection rates for those who did enroll. Participants had generally positive views of passive monitoring.

Purpose: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non-small cell lung cancer (NSCLC) with PD-L1 expression ≥50%. We sought to find out if a machine learning (ML) algorithm can perform better as a predictive biomarker than PD-L1 alone.

Methods: Using a real-world, nationwide electronic health record-derived deidentified database of 38,048 patients with advanced NSCLC, we trained binary prediction algorithms to predict likelihood of 12-month progression-free survival (PFS; 12-month PFS) and 12-month overall survival (OS; 12-month OS) from initiation of first-line therapy. We evaluated the algorithms by calculating the AUC on the test set. We plotted Kaplan-Meier curves and fit Cox survival models comparing survival between patients who were classified as low-risk (LR) for 12-month disease progression or 12-month mortality versus those classified as high-risk.

Results: The ML algorithms achieved an AUC of 0.701 (95% CI, 0.689 to 0.714) and 0.718 (95% CI, 0.707 to 0.730) for 12-month PFS and 12-month OS, respectively. Patients in the LR group had lower 12-month disease progression (hazard ratio [HR], 0.47 [95% CI, 0.45 to 0.50]; P < .001) and 12-month all-cause mortality (HR, 0.31 [95% CI, 0.29 to 0.34]; P < .0001) compared with the high-risk group. Patients deemed LR for disease progression and mortality on IO monotherapy were less likely to progress (HR, 0.53 [95% CI, 0.46 to 0.61]; P < .0001) or die (HR, 0.30 [95% CI, 0.24 to 0.37]; P < .001) compared with the high-risk group.

Conclusion: An ML algorithm can more accurately predict response to first-line therapy, including IO monotherapy, in patients with advanced NSCLC, compared with PD-L1 alone. ML may better aid clinical decision making in oncology than a single biomarker.

Purpose: To explore the perceived utility and effect of simplified radiology reports on oncology patients' knowledge and feasibility of large language models (LLMs) to generate such reports.

Materials and methods: This study was approved by the Institute Ethics Committee. In phase I, five state-of-the-art LLMs (Generative Pre-Trained Transformer-4o [GPT-4o], Google Gemini, Claude Opus, Llama-3.1-8B, and Phi-3.5-mini) were tested to simplify 50 oncology computed tomography (CT) report impressions using five distinct prompts with each LLM. The outputs were evaluated quantitatively using readability indices. Five LLM-prompt combinations with best average readability scores were also assessed qualitatively, and the best LLM-prompt combination was selected. In phase II, 100 consecutive oncology patients were randomly assigned into two groups: original report (received original report impression) and simplified report (received LLM-generated simplified versions of their CT report impressions under the supervision of a radiologist). A questionnaire assessed the impact of these reports on patients' knowledge and perceived utility.

Results: In phase I, Claude Opus-Prompt 3 (explain to a 15-year-old) performed slightly better than other LLMs, although scores for GPT-4o, Gemini, Claude Opus, and Llama-3.1 were not significantly different (P > .0033 on Wilcoxon signed-rank test with Bonferroni correction). In phase II, simplified report group patients demonstrated significantly better knowledge of primary site and extent of their disease as well as showed significantly higher confidence and understanding of the report (P < .05 for all). Only three (of 50) simplified reports required corrections by the radiologist.

Conclusion: Simplified radiology reports significantly enhanced patients' understanding and confidence in comprehending their medical condition. LLMs performed very well at this simplification task; therefore, they can be potentially used for this purpose, although there remains a need for human oversight.

Purpose: Lung cancer screening (LCS) has the potential to reduce mortality and detect lung cancer at its early stages, but the high false-positive rate associated with low-dose computed tomography (LDCT) for LCS acts as a barrier to its widespread adoption. This study aims to develop computable phenotype (CP) algorithms on the basis of electronic health records (EHRs) to identify individual's eligibility for LCS, thereby enhancing LCS utilization in real-world settings.

Materials and methods: The study cohort included 5,778 individuals who underwent LDCT for LCS from 2012 to 2022, as recorded in the University of Florida Health Integrated Data Repository. CP rules derived from LCS guidelines were used to identify potential candidates, incorporating both structured EHR and clinical notes analyzed via natural language processing. We then conducted manual reviews of 453 randomly selected charts to refine and validate these rules, assessing CP performance using metrics, for example, F1 score, specificity, and sensitivity.

Results: We developed an optimal CP rule that integrates both structured and unstructured data, adhering to the US Preventive Services Task Force 2013 and 2020 guidelines. This rule focuses on age (55-80 years for 2013 and 50-80 years for 2020), smoking status (current, former, and others), and pack-years (≥30 for 2013 and ≥20 for 2020), achieving F1 scores of 0.75 and 0.84 for the respective guidelines. Including unstructured data improved the F1 score performance by up to 9.2% for 2013 and 12.9% for 2020, compared with using structured data alone.

Conclusion: Our findings underscore the critical need for improved documentation of smoking information in EHRs, demonstrate the value of artificial intelligence techniques in enhancing CP performance, and confirm the effectiveness of EHR-based CP in identifying LCS-eligible individuals. This supports its potential to aid clinical decision making and optimize patient care.