JCO Clinical Cancer Informatics最新文献

Purpose: Compare the association of individual comorbidities, comorbidity indices, and survival in older adults with non-Hodgkin lymphoma (NHL), including in specific NHL subtypes.

Methods: Data source was SEER-Medicare, a population-based registry of adults age 65 years and older with cancer. We included all incident cases of NHL diagnosed during 2008-2017 who met study inclusion criteria. Comorbidities were classified using the three-factor risk estimate scale (TRES), Charlson comorbidity index (CCI), and National Cancer Institute (NCI) comorbidity index categories and weights. Overall survival (OS) and lymphoma-specific survival, with death from other causes treated as a competing risk, were estimated using the Kaplan-Meier method from time of diagnosis. Multivariable Cox models were constructed, and Harrel C-statistics were used to compare comorbidity models. A two-sided P value of <.05 was considered significant.

Results: A total of 40,486 patients with newly diagnosed NHL were included. Patients with aggressive NHL had higher rates of baseline comorbidity. Despite differences in baseline comorbidity between NHL subtypes, cardiovascular, pulmonary, diabetes, and renal comorbidities were frequent and consistently associated with OS in most NHL subtypes. These categories were used to construct a candidate comorbidity score, the non-Hodgkin lymphoma 5 (NHL-5). Comparing three validated comorbidity scores, TRES, CCI, NCI, and the novel NHL-5 score, we found similar associations with OS and lymphoma-specific survival, which was confirmed in sensitivity analyses by NHL subtypes.

Conclusion: The optimal measure of comorbidity in NHL is unknown. Here, we demonstrate that the three-category TRES and five-category NHL-5 scores perform as well as the 14-16 category CCI and NCI scores in terms of association with OS and lymphoma-specific survival. These simple scores could be more easily used in clinical practice without prognostic loss.

Purpose: Accurate documentation of lesions during transurethral resection of bladder tumors (TURBT) is essential for precise diagnosis, treatment planning, and follow-up care. However, optimizing schematic documentation techniques for bladder lesions has received limited attention.

Materials and methods: This prospective observational study used a cMDX-based documentation system that facilitates graphical representation, a lesion-specific questionnaire, and heatmap analysis with a posterization effect. We designed a graphical scheme for bladder covering bladder landmarks to visualize anatomic features and to document the lesion location. The lesion-specific questionnaire was integrated for comprehensive lesion characterization. Finally, spatial analyses were applied to investigate the anatomic distribution patterns of bladder lesions.

Results: A total of 97 TURBT cases conducted between 2021 and 2023 were included, identifying 176 lesions. The lesions were distributed in different bladder areas with varying frequencies. The distribution pattern, sorted by frequency, was observed in the following areas: posterior, trigone, lateral right and anterior, and lateral left and dome. Suspicious levels were assigned to the lesions, mostly categorized either as indeterminate or moderate. Lesion size analysis revealed that most lesions fell between 5 and 29 mm.

Conclusion: The study highlights the potential of schematic documentation techniques for informed decision making, quality assessment, primary research, and secondary data utilization of intraoperative data in the context of TURBT. Integrating cMDX and heatmap analysis provides valuable insights into lesion distribution and characteristics.

Purpose: The standard practice for limited-stage hepatocellular carcinoma (HCC) is the resection or the use of local ablative techniques, such as radiofrequency ablation (RFA). The outcome after RFA depends on a complex interaction between the patient's general condition, hepatic function, and disease stage. In this study, we aimed to explore using a machine learning model to predict the response.

Patients and methods: A retrospective study was conducted for patients with RFA for a localized HCC between 2018 and 2022. The collected clinical, radiologic, and laboratory data were explored using Python and XGBoost. They were split into a training set (70%) and a validation set (30%). The primary end point of this study was to predict the probability of achieving favorable outcomes 12 months after RFA. Favorable outcomes were defined as the patient was alive and HCC was controlled.

Results: One hundred and eleven patients were eligible for the study. Males were 78 (70.3%) with a median age of 57 (range of 43-81) years. Favorable outcome was seen in 62 (55.9%) of the patients. The 1-year survival rate and control rate were 94.6%, and 61.3%, respectively. The final model harbored an accuracy and an AUC of 90.6% and 0.95, respectively, for the training set, while they were 78.9% and 0.80, respectively, for the validation set.

Conclusion: Machine learning can be a predictive tool for the outcome after RFA in patients with HCC. Further validation by a larger study is necessary.

Purpose: As the onset of cancer recurrence is not explicitly recorded in the electronic health record (EHR), a high volume of manual chart review is required to detect the cancer recurrence. This study aims to develop an automatic rule-based algorithm for detecting ovarian cancer (OC) recurrence on the basis of minimally preprocessed EHR data.

Methods: The automatic rule-based recurrence detection algorithm (Auto-Recur), using notes on image reading (positron emission tomography-computed tomography [PET-CT], CT, magnetic resonance imaging [MRI]), biomarker (CA125), and treatment information (surgery, chemotherapy, radiotherapy), was developed to detect the first OC recurrence. Auto-Recur contains three single algorithms (images, biomarkers, treatments) and hybrid algorithms (combinations of the single algorithms). The performance of Auto-Recur was assessed using sensitivity, specificity, and accuracy of the recurrence time detected. The recurrence-free survival probabilities were estimated and compared with the retrospective chart review results.

Results: The proposed Auto-Recur considerably reduced human resources and time; it saved approximately 1,340 days when scaled to 100,000 patients compared with the conventional retrospective chart review. The hybrid algorithm on the basis of a combination of image, biomarker, and treatment information was the most efficient (sensitivity: 93.4%, specificity: 97.4%) and precisely captured recurrence time (average time error: 8.5 days). The estimated 3-year recurrence-free survival probability (44%) was close to the estimates by the retrospective chart review (45%, log-rank P value = .894).

Conclusion: Our rule-based algorithm effectively captured the first OC recurrence from large-scale EHR while closely approximating the recurrence-free survival estimates obtained by conventional retrospective chart reviews. The study findings facilitate large-scale EHR analysis, enhancing clinical research opportunities.

Purpose: Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (¹⁸F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using ¹⁸F-FDG PET/CT scans before thoracic radiation therapy.

Methods: Pretreatment ¹⁸F-FDG PET/CT scans from three study populations (N = 100, N = 39, N = 70) were used, comprising two single-institutional protocols and one publicly available data set. A clinician (V.J.) classified the PET/CT scans per clinical cardiac guidelines as no uptake, diffuse uptake, or focal uptake. The heart was delineated, and 210 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. Training data were divided into training (80%)/validation (20%) sets. Feature reduction was performed using the Wilcoxon test, hierarchical clustering, and recursive feature elimination. Ten-fold cross-validation was carried out for training, and the accuracy of the models to predict clinical cardiac assessment was reported.

Results: From 202 of 209 scans, cardiac FDG uptake was scored as no uptake (39.6%), diffuse uptake (25.3%), and focal uptake (35.1%), respectively. Sixty-two independent radiomics features were reduced to nine clinically pertinent features. The best model showed 93% predictive accuracy in the training data set and 80% and 92% predictive accuracy in two external validation data sets.

Conclusion: This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care ¹⁸F-FDG PET/CT scans, showing good predictive accuracy. The radiomics model has the potential to provide an automated method to predict existing cardiac conditions and provide an early functional biomarker to identify patients at risk of developing cardiac complications after radiotherapy.

Purpose: Information on concerns that young adults (YAs) with cancer face when receiving care outside of specialized treatment centers is needed to increase equitable care to YAs at greater risk of marginalization by the health care system. The current study compared distress and unmet needs at the time of clinic visit between YAs receiving care from three different cancer clinics: (1) a National Cancer Institute-designated center, (2) a community-based clinic, and (3) a county hospital outpatient clinic.

Methods: The Adolescent and Young Adult Psycho-Oncology Screening Tool (AYA-POST) was administered to measure distress and cancer-related concerns of YAs in active treatment. A one-way analysis of variance (ANOVA) compared distress scores by treatment site. A Fisher's exact test compared the number of participants endorsing each item on the Needs Assessment Checklist from each site. A simple linear regression determined the association between distress and number of items endorsed on the Needs Assessment Checklist.

Results: Ninety-seven participants completed the AYA-POST, endorsing, on average, 11 concerns. Fisher's exact test showed significant differences between sites in the proportion of participants endorsing eight items: boredom (P < .001), eating/appetite (P < .001), nausea/vomiting (P < .001), financial concern (P = .002), hopelessness/helplessness (P = .03), confidentiality (P = .04), sibling concern (P = .04), and insurance (P = .05). The simple linear regression model was significant (F(1, 94) = 39.772, P < .001, R² = 0.297), indicating the number of unmet needs accounted for almost 30% of the variance in distress. The one-way ANOVA was not significant (F(2, 93) = 1.34, P = .267).

Conclusion: Social determinants of health can influence the number and type of unmet needs experienced, affecting distress and other outcomes and underscoring the importance of timely, effective, age-appropriate screening and intervention for distress and unmet needs in YAs with cancer.

Purpose: Real-world lung cancer data in administrative claims databases often lack staging information and specific diagnostic codes for lung cancer histology subtypes. This study updates and validates Turner's 2017 treatment-based algorithm using more recent claims and electronic health record (EHR) data.

Methods: This study used Optum's deidentified Market Clarity Data of linked medical and pharmacy claims with EHR data. Eligible patients had an incident lung cancer diagnosis (January 2014-December 2020) and ≥one valid histology code for lung cancer 30 days before to 60 days after diagnosis. Histology and stage information from the EHR were used to evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated the Turner algorithm using cohort 1 patients diagnosed between June 2014 and October 2015 (step 1) and between November 2015 and December 2020 after approval of immunotherapies (step 2). Next, we evaluated cohort 2 patients diagnosed between November 2015 and December 2020 using an updated algorithm incorporating the latest US treatment guidelines (step 3), and compared the results for cohort 2 (Turner algorithm, step 2 patients). Furthermore, an algorithm to determine early NSCLC (eNSCLC; stage I-III) versus metastatic or advanced/metastatic non-small cell lung cancer (stage IV) was evaluated among patients with available histology and stage information.

Results: A total of 5,012 patients were included (cohort 1, step 1: n = 406; cohort 1, step 2: n = 2,573; cohort 2, step 3: n = 2,744). The updated algorithm showed improved performance relative to the previous Turner algorithm for sensitivity (0.920-0.932), specificity (0.865-0.923), PPV (0.976-0.988), and NPV (0.640-0.673). The eNSCLC algorithm showed high specificity (0.874) and relatively low sensitivity (0.539).

Conclusion: An updated treatment-based algorithm identifying patients with incident NSCLC was validated using EHR data and distinguished lung cancer subtypes in claims databases when EHR data were not available.

Purpose: The gender, age, lens culinaris agglutinin-reactive fraction of alphafetoprotein, alphafetoprotein, des-gamma-carboxyprothrombin (GALAD) score is a biomarker-based statistical model for the serologic diagnosis of hepatocellular carcinoma (HCC) that has been developed and validated using the case-control approach with a view to early detection. Performance has, however, been suboptimal in the first prospective studies which better reflect the real-world situation. In this article, we report the application of machine learning to a large, prospectively accrued, HCC surveillance data set.

Patients and methods: Models were built on a cohort of 3,473 patients with chronic liver disease within a rigorous surveillance program between 1998 and 2014, during which 459 patients with HCC were detected. Two random forest (RF) models were trained. The first RF model uses the same variables as the original GALAD model (GALAD-RF); the second is based on routinely available clinical and laboratory features (RF-practical). For comparison, we evaluated a logistic regression GALAD model trained on this longitudinal prospective data set (termed GALAD-Ogaki).

Results: Models were evaluated using a repetitive cross-validation approach with the metrics averaged over 100 independent runs. As judged by area under the receiver operator curve (AUROC) and F1 score, the GALAD RF model significantly outperformed the original GALAD model. The RF-practical model also outperformed the original GALAD model in terms of both AUROC and F1 score, and both models outperformed the individual biomarkers. An online web application that implemented the GALAD-RF and RF-practical models is presented.

Conclusion: RF-based models improve on the diagnostic performance of the original GALAD model in the setting of a standard HCC surveillance program. Further prospective validation studies are warranted using these models and could be expanded to offer prediction of risk of HCC development over defined periods of time.

Standardizing image-data preparation practices to improve accuracy/consistency of AI diagnostic tools.

New publication provides insights into the impact of disability on outcomes in older adults with multiple myeloma.