JCO Clinical Cancer Informatics最新文献

Purpose: Biomarkers, or specific somatic alterations, are increasingly required for clinical trial eligibility. Finding and enrolling patients with these biomarkers is essential not only for continuous progress in the treatment of disease but also for democratizing clinical trial participation. Here, we use data from the National Cancer Institute Clinical Trials Reporting Program (NCI CTRP), combined with large language model applications, to survey the current landscape of cancer clinical trials.

Methods: We extracted 20,894 trials from Cancer.gov from the application programming interface (API) of the NCI CTRP. We quantified biomarker rates in cancer subtypes, described the geographic distribution of trial sites, and identified failure causes for these trials. Finally, we built an application from this API to match patients with clinical trials.

Results: We showed that 5,044 of the 20,894 interventional clinical trials contained biomarker eligibility data and trials tended to cluster around large academic centers and cities. We identified 630 biomarkers in 36 cancer subtypes and show that most biomarkers are used as eligibility criteria for multiple cancer subtypes. We highlight that the difficulties with accrual and sponsorship were the most common reason for discontinuing clinical trials. Finally, we demonstrate a novel method to automatically match natural language queries with eligible clinical trials, NCI Clinical Trials Navigator.

Conclusion: A survey of our clinical genomics showed that many individuals likely have mutations that would make them eligible for biomarker-driven trials. We used the NCI Clinical Trials database to show that the distribution of biomarker trials across the United States limits access for many patients and likely leads to the frequent trial termination because of inadequate accrual. Finally, we built an automated publicly available tool that can improve patient-to-trial biomarker-based matching.

Purpose: Electronic health record (EHR) systems aim to improve efficiency, care coordination, and patient safety, yet implementation often introduces workflow challenges and staff burden. In 2024, the Cancer Centre of Southeastern Ontario (CCSEO), a regional academic cancer center in Canada, transitioned from a hybrid paper-electronic system to a fully integrated regional EHR. Although hospital EHR adoption has been studied, limited research has examined its impact within ambulatory oncology care, particularly among nonphysician staff, or how institutions responded to the findings. Our study explored oncology healthcare worker perspectives on EHR implementation at CCSEO and identified resulting quality-improvement (QI) initiatives.

Methods: Using purposeful maximum variation sampling, we recruited clinical, administrative, and research staff. Semistructured interviews explored workflow efficiency, documentation burden, staff wellness, patient safety, communication, and training. Data were audio-recorded, transcribed, and analyzed thematically using MAXQDA.

Results: Nineteen interviews were conducted until thematic saturation. Three major themes emerged. (1) Efficiency and workflow: Staff valued consolidated records and regional connectivity but reported navigation complexity, time burden, duplicate orders, reliance on multiple programs, and frequent workarounds. (2) Staff and patient wellness: Staff noted limited training, increased workload, cognitive overload, and reliance on peer support contributed to burnout. (3) Patient safety: Identified risks included order and medication errors, communication breakdowns, poor system visualization, imaging delays, and wristband or labeling issues. Several QI initiatives were implemented in response, including education and navigation rounds, formation of working groups, and integration of artificial intelligence.

Conclusion: EHR implementation introduced both benefits and challenges in oncology workflows. Findings informed multidisciplinary QI initiatives targeting role-specific training, workflow optimization, and safety, offering a framework for other cancer centers transitioning to new EHR systems.

Purpose: This trial aims to investigate the effectiveness of online digital intervention in patients with non-small cell lung cancer (NSCLC) in terms of adverse events (AEs) and quality of life (QoL).

Methods: This randomized trial recruited 200 patients with advanced NSCLC (March 2022-October 2023). All patients received standard-of-care precise treatment, predominantly immunochemotherapy. The study was designed to assess AEs and QoL improvement. Through the CareAcross online platform, all patients received information about their disease and treatment and reported any of the 22 predefined AEs at any time. Patients were randomly assigned 1:1 in the intervention (A) and control (B) arm; patients in arm A automatically received, additionally, evidence-based guidance for the reported AEs. EuroQol 5-dimension 5-level responses were collected at baseline and at each treatment cycle. Resulting scores were compared between baseline and after the sixth cycle. In addition, patient case-level hospitalization data were collected and costs were estimated based on reimbursed costs as defined by the Ministry of Health, enabling a post hoc analysis.

Results: Clinical characteristics were well-balanced. More AEs were reported by patients online versus to their clinicians (P < .01). Among the 22 AEs, 17 improved more in arm A, with the improvement in rash and stomatitis being statistically significant. In QoL, there was no improvement in any of the five EuroQol 5-Dimension dimensions. Digital intervention was cost-saving with lower mean costs for hospitalization (P < .001). Overall response rate, progression-free survival, and overall survival were not statistically different between the two arms, ensuring comparable clinical outcome.

Conclusion: Digital oncology tends to improve selected AEs and is cost saving. Patients report, digitally, more informative AEs. Digital oncology can be a complementary tool to the oncology team and warrants further exploration.

Purpose: Routinely collected administrative data provide insights into health care utilization and outcomes but lack detailed clinical information, such as the specific site and intent of radiation therapy (RT). This study aimed to validate claims-based algorithms to accurately identify thoracic RT (TRT) and curative-intent RT in administrative databases.

Methods: Patients at our institution with lung cancer and any RT Current Procedural Terminology (CPT) code from October 2015 to January 2024 were analyzed. RT claims were organized by treatment episode, and RT details were manually abstracted from the electronic health record to classify episodes as TRT or non-TRT and curative or noncurative. A priori algorithms were defined as the presence of respiratory motion management codes, >14 treatment codes (except for stereotactic body RT [SBRT] courses), with or without exclusive thoracic malignancy diagnosis codes. Positive predictive value (PPV) was computed for each episode, stratified by modality (three-dimensional conformal RT [3DCRT], intensity-modulated RT [IMRT], and SBRT). Algorithms were considered acceptable if the lower bound of the Clopper-Pearson 95% CI for PPV exceeded 70%.

Results: A total of 3,846 RT episodes were analyzed. The primary a priori TRT algorithm achieved a PPV of 97% (95% CI, 96 to 98) for IMRT, 99% (95% CI, 97 to 99) for SBRT, and 87% (95% CI, 81 to 92) for 3DCRT. Performance declined when exclusive thoracic malignancy diagnosis codes were excluded. For curative-intent RT, PPVs were 87% for IMRT, 90% for SBRT, and 55% for 3DCRT.

Conclusion: Clinically informed algorithms can accurately identify TRT in claims data, achieving high PPVs particularly for IMRT and SBRT courses. These algorithms can be applied in claims databases to assess RT toxicity and effectiveness. External validation across diverse data sets will be important to confirm generalizability.

Purpose: It is essential to detect and segment liver tumors to guide treatment and track disease progression. To reduce the need for large annotated data sets, we present an end-to-end pipeline that uses self-supervised pretraining to improve segmentation and then classifies tumor types with a separate pretrained classifier applied to the segmented tumor regions.

Methods: First, we pretrained the encoder of a transformer-based network using a self-supervised approach on unlabeled abdominal computed tomography images. Subsequently, we fine-tuned the segmentation network to segment the liver and tumors, and the tumor regions were classified using a pretrained convolutional neural network (Inception-v3 architecture) as intrahepatic cholangiocarcinoma (ICC), hepatocellular carcinoma (HCC), or colorectal liver metastases (CRLMs). We evaluated 459 images (155 HCC, 107 ICC, 197 CRLM). For external testing, we used an independent public data set (n = 40).

Results: Averaged across HCC, ICC, and CRLM, in comparison with a supervised baseline (no pretraining), self-supervised pretraining improved the liver Dice similarity coefficient (DSC) by 6.4 percentage points and reduced the 95th-percentile Hausdorff distance (HD₉₅) by 32.97 mm. For tumors, the DSC increased by 6.0 percentage points and the HD₉₅ decreased by 3.2 mm. Tumor type classification achieved AUC 0.98 (95% CI, 0.96 to 1.00) and accuracy 96% (95% CI, 92% to 99%). Segmentation performance on the external data was close to the internal cohort with tumor DSC 0.73, intersection over union (IoU) 0.60, and HD₉₅ 30.98 mm and liver DSC 0.91, IoU 0.83, and HD₉₅ 29.67 mm.

Conclusion: The proposed self-supervised, end-to-end pipeline improves liver tumor segmentation and provides accurate tumor type classification, supporting reliable radiologic assessment, treatment planning, and improved prognostication for patients with liver cancer.

Purpose: The University of Wisconsin Population Health Institute (PHI) Model of Health, grounded in models developed over a decade ago, provides a framework for prioritizing health-related investments including setting agendas, implementing policies, and sharing resources for improving community health and health equity. The model includes multiple determinants of health and two broad health outcomes (length and quality of life). We adapted the PHI Model of Health to cancer outcomes.

Methods: Using county-level publicly available data, health factor summary measures were derived in three areas: health infrastructure including health promotion and clinical care, physical environment, and social and economic factors. A composite health factor z-score was calculated as the weighted (40%, 15%, and 45%, respectively) average of the summary measures for each county, and k-means clustering was used to create unequally sized county groups with lower (healthier) to higher (less healthy) z-scores. We fit age-adjusted negative binomial regression models to estimate rate ratios and 95% CI for cancer mortality in relation to county health factor cluster.

Results: Age-adjusted cancer mortality rates increased across the 10 county health factor clusters for all-cancers as well as for lung, colorectal, breast, and prostate cancers. Rate ratios generally increased across the 10 health factor clusters for all cancers combined and for specific cancer types. Compared with counties with the most favorable health factor conditions, the counties with the least favorable conditions had an all-cancer mortality rate ratio of 1.49 (95% CI, 1.39 to 1.60).

Conclusion: The PHI model of health adapted to cancer outcomes provides an approach for linking community-specific conditions to the interventions that hold promise to directly address drivers of the cancer burden.

Purpose: Real-world data (RWD) are increasingly used in oncology research, regulatory decisions, and clinical practice; however, variability in data quality and lack of standardization remain major limitations. This study assessed the readiness of oncology RWD from Saudi health care centers for standardization and evaluated their completeness and accuracy.

Methods: Deidentified electronic health records for adult patients (18 years and older) diagnosed with breast cancer, thyroid cancer, colorectal cancer, gastric cancer, hepatocellular carcinoma, or renal cell carcinoma were extracted from five health care centers within the Saudi Real-World Evidence Network. Readiness for standardization was evaluated by assessing alignment with data elements in the Minimal Common Oncology Data Elements (mCODE) framework, a standardized and clinically focused oncology data model. Data quality was evaluated using two dimensions: completeness, defined as the proportion of patients with at least one entered value for each element; and accuracy, defined as the proportion of correct entries based on verification checks (including plausibility and consistency). Outcomes were calculated at the element level and weighted to generate domain- and center-level proportions.

Results: A total of 20,671 oncology patients were included. Overall weighted alignment with mCODE domains was moderate (62.43%). The patient domain showed the highest alignment (71.43%), whereas the outcome domain exhibited significant gaps. Data completeness was low to moderate (49.02%), with higher levels in common cancers (54.33%) than in rare cancers (51.50%). Data accuracy was high overall (95.03%), with rare cancers showing higher accuracy (98.76%) than common cancers (94.62%).

Conclusion: Saudi oncology RWD show moderate alignment with mCODE, with consistently high accuracy across domains. However, gaps in data completeness highlight the need for broader adoption of standardized data frameworks to support interoperability and enable nationwide research and regulatory use.

Purpose: Our study is motivated by evaluating the role of hematopoietic cell transplantation (HCT) after chimeric antigen receptor T-cell (CAR-T) therapy for ALL, a debated topic. Because patients may receive HCT at different times after CAR-T infusion or never, HCT post-CAR-T should be considered as a time-varying covariate (TVC).

Methods: Standard Cox models and Kaplan-Meier (KM) curves (naïve method) assume that TVC status is known and fixed at baseline, which can yield biased estimates. Landmark analysis is a popular alternative but depends on a chosen landmark time. Time-dependent (TD) Cox model is better suited for TVC although visualizing survival curves is complex. The newly proposed Smith-Zee method generates appropriate survival curves from TD Cox models.

Results: To address these challenges, we developed an open-source R Shiny tool integrating multiple models (naïve Cox, landmark Cox, and TD Cox) and curves (naïve KM, landmark KM, Smith-Zee, and Extended KM) to facilitate TVC analysis. Reanalysis of post-CAR-T HCT's effect on leukemia-free survival (LFS) showed consistent results between naïve and TD Cox models, whereas landmark analyses varied by landmark time. A separate data analysis of chronic graft-versus-host disease and survival showed that substantial differences emerged across statistical methods. Simulations revealed increased bias in naïve methods when TVC changed late and minimal bias when TVC changes occurred early relative to time to events.

Conclusion: We recommend TD Cox models and Smith-Zee curves for robust TVC analysis. Our R Shiny tool supports standardized analyses without requiring data sharing, thereby promoting collaboration across different institutions and providing a practical tool to advance survival analysis in oncology research.

Purpose: Cancer clinical trial enrollment remains critically low at 5%-7% of adult patients despite exponential growth in available trials. Manual patient-trial matching represents a fundamental bottleneck, whereas current artificial intelligence (AI) and machine learning patient-trial matching systems lack data standardization and compatibility across health systems. We developed and validated a semiautomated clinical trial patient matching (CTPM) tool to improve recruitment efficiency and scalability.

Methods: We created a hybrid rules-based and natural language processing (NLP)-based pipeline that automatically screens patients using structured and unstructured electronic health record data standardized to the Observational Medical Outcomes Partnership (OMOP) common data model. CTPM performance was first evaluated on one metastatic colorectal cancer (CRC) trial by comparing CTPM accuracy and efficiency to manual chart review. Following the single-trial validation, we then implemented the system across 29 clinical trials spanning multiple cancer specialties and phases.

Results: For the single CRC trial, CTPM achieved 94% retrospective and 88% prospective accuracy, matching gold standard clinical chart review with 100% sensitivity. Implementation reduced chart review workload 10-fold and screening time by 41% (3.1 to 1.8 minutes per chart) for those patients who did undergo review. Since September 2022, the system has screened 98,348 patients across 29 trials, identifying 825 eligible candidates and facilitating 117 patient enrollments with 9%-37% consent rates.

Conclusion: This AI and NLP tool demonstrates improved efficiency in clinical trial recruitment by enabling research teams to focus on qualified candidates rather than exhaustive chart reviews. The OMOP-based framework supports scalability across health systems, with potential to address enrollment challenges that limit patient access to clinical trials.

Purpose: Manual extraction of treatment outcomes from unstructured oncology clinical notes is a significant challenge for real-world evidence (RWE) generation. This study aimed to develop and evaluate a robust natural language processing (NLP) system to automatically extract cancer treatments and their associated RECIST-based response categories (complete response, partial response, stable disease, and progressive disease) from non-small cell lung cancer (NSCLC) clinical notes.

Methods: This retrospective NLP development and validation study used a corpus of 250 NSCLC oncology notes from University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center, annotated by physician experts. An end-to-end NLP pipeline was designed, integrating a rule-based module for entity extraction (treatments and responses) and a machine learning module using biomedical clinical bidirectional encoder representations from transformers for relation classification. The system's performance was evaluated on a held-out test set, with partial external validation for relation extraction on a Mayo Clinic data set.

Results: The NLP system achieved high overall accuracy. On the UPMC test set (64 notes), the relation classification model attained an area under the receiver operating characteristic curve of 0.94 and an F1 score of 0.92 for linking treatments with documented responses. The rule-based entity extraction demonstrated a macro-averaged F1 score of 0.87 (precision 0.98, recall 0.81). Although precision was high for chemotherapy and most response types (1.00), recall for cancer surgery was 0.45. External validation at Mayo Clinic showed moderate relation extraction F1 scores (range: 0.51-0.64).

Conclusion: The proposed NLP system can reliably extract structured treatment and response information from unstructured NSCLC oncology notes with high accuracy. This automated approach can assist in abstracting critical cancer treatment outcomes from clinical narrative text, thereby streamlining real-world data analysis and supporting the generation of RWE in oncology.