JCO Clinical Cancer Informatics最新文献

{"title":"Validation of Non-Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model.","authors":"Rachel C Kenney, Xiaoren Chen, Kazuki Shintani, Clara Gagnon, John Liu, Stacey DaCosta Byfield, Lorre Ochs, Anne-Marie Currie","doi":"10.1200/CCI.23.00099","DOIUrl":"10.1200/CCI.23.00099","url":null,"abstract":"<p><strong>Purpose: </strong>Limited studies have used natural language processing (NLP) in the context of non-small cell lung cancer (NSCLC). This study aimed to validate the application of an NLP model to an NSCLC cohort by extracting NSCLC concepts from free-text medical notes and converting them to structured, interpretable data.</p><p><strong>Methods: </strong>Patients with a lung neoplasm, NSCLC histology, and treatment information in their notes were selected from a repository of over 27 million patients. From these, 200 were randomly selected for this study with the longest and the most recent note included for each patient. An NLP model developed and validated on a large solid and blood cancer oncology cohort was applied to this NSCLC cohort. Two certified tumor registrars and a curator abstracted concepts from the notes: neoplasm, histology, stage, TNM values, and metastasis sites. This manually abstracted gold standard was compared with the NLP model output. Precision and recall scores were calculated.</p><p><strong>Results: </strong>The NLP model extracted the NSCLC concepts with excellent precision and recall with the following scores, respectively: Lung neoplasm 100% and 100%, NSCLC histology 99% and 88%, histology correctly linked to neoplasm 98% and 79%, stage value 98.8% and 92%, stage TNM value 93% and 98%, and metastasis site 97% and 89%. High precision is related to a low number of false positives, and therefore, extracted concepts are likely accurate. High recall indicates that the model captured most of the desired concepts.</p><p><strong>Conclusion: </strong>This study validates that Optum's oncology NLP model has high precision and recall with clinical real-world data and is a reliable model to support research studies and clinical trials. This validation study shows that our nonspecific solid tumor and blood cancer oncology model is generalizable to successfully extract clinical information from specific cancer cohorts.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2300099"},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Optimization of a Bladder Cancer Algorithm Using SEER-Medicare Claims Data.","authors":"John L Gore, Phoebe Wright, Vanessa Shih, Nancy N Chang, Sina Noshad, Gabriel G Rey, Steven Wang, Sujata Narayanan","doi":"10.1200/CCI.24.00073","DOIUrl":"10.1200/CCI.24.00073","url":null,"abstract":"<p><strong>Purpose: </strong>Categorizing patients with cancer by their disease stage can be an important tool when conducting administrative claims-based studies. As claims databases frequently do not capture this information, algorithms are increasingly used to define disease stage. To our knowledge, to date, no study has used an algorithm to categorize patients with bladder cancer (BC) by disease stage (non-muscle-invasive BC [NMIBC], muscle-invasive BC [MIBC], or locally advanced/metastatic urothelial carcinoma [la/mUC]) in a US-based health care claims database.</p><p><strong>Methods: </strong>A claims-based algorithm was developed to categorize patients by disease stage on the basis of the administrative claims portion of the SEER-Medicare linked data. The algorithm was validated against a reference SEER registry, and the algorithm's parameters were iteratively modified to improve its performance. Patients were included if they had an initial diagnosis of BC between January 2016 and December 2017 recorded in SEER registry data. Medicare claims data were available for these patients until December 31, 2019. The algorithm was evaluated by assessing percentage agreement, Cohen's kappa (κ), specificity, positive predictive value (PPV), and negative predictive value (NPV) against the SEER categorization.</p><p><strong>Results: </strong>A total of 15,484 patients with SEER-confirmed BC were included: 10,991 (71.0%) with NMIBC, 3,645 (23.5%) with MIBC, and 848 (5.5%) with la/mUC. After multiple rounds of algorithm optimization, the final algorithm had an agreement of 82.5% with SEER, with a κ of 0.58, a PPV of 87.0% for NMIBC, and 76.8% for MIBC and a high NPV for la/mUC of 98.0%.</p><p><strong>Conclusion: </strong>This claims-based algorithm could be a useful approach for researchers conducting claims-based studies categorizing patients with BC at diagnosis.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400073"},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11421559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction.","authors":"Nuno M Rodrigues, José Guilherme de Almeida, Ana Rodrigues, Leonardo Vanneschi, Celso Matos, Maria V Lisitskaya, Aycan Uysal, Sara Silva, Nickolas Papanikolaou","doi":"10.1200/CCI.23.00180","DOIUrl":"10.1200/CCI.23.00180","url":null,"abstract":"<p><strong>Purpose: </strong>Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentation and the extraction of radiomic features considers it as an independent and sequential process. However, it is not necessary to adhere to this viewpoint. In this study, we show that besides generating masks from which radiomic features can be extracted, prostate segmentation and reconstruction models provide valuable information in their feature space, which can improve the quality of radiomic signatures models for disease aggressiveness classification.</p><p><strong>Materials and methods: </strong>We perform 2,244 experiments with deep learning features extracted from 13 different models trained using different anatomic zones and characterize how modeling decisions, such as deep feature aggregation and dimensionality reduction, affect performance.</p><p><strong>Results: </strong>While models using deep features from full gland and radiomic features consistently lead to improved disease aggressiveness prediction performance, others are detrimental. Our results suggest that the use of deep features can be beneficial, but an appropriate and comprehensive assessment is necessary to ensure that their inclusion does not harm predictive performance.</p><p><strong>Conclusion: </strong>The study findings reveal that incorporating deep features derived from autoencoder models trained to reconstruct the full prostate gland (both zonal models show worse performance than radiomics only models), combined with radiomic features, often lead to a statistically significant increase in model performance for disease aggressiveness classification. Additionally, the results also demonstrate that the choice of feature selection is key to achieving good performance, with principal component analysis (PCA) and PCA + relief being the best approaches and that there is no clear difference between the three proposed latent representation extraction techniques.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2300180"},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.","authors":"Meghan E Lindsay, Sophia de Oliveira, Kate Sciacca, Charlotta Lindvall, Prasanna J Ananth","doi":"10.1200/CCI.24.00134","DOIUrl":"10.1200/CCI.24.00134","url":null,"abstract":"<p><strong>Purpose: </strong>Data on end-of-life care (EOLC) quality, assessed through evidence-based quality measures (QMs), are difficult to obtain. Natural language processing (NLP) enables efficient quality measurement and is not yet used for children with serious illness. We sought to validate a pediatric-specific EOLC-QM keyword library and evaluate EOLC-QM attainment among childhood cancer decedents.</p><p><strong>Methods: </strong>In a single-center cohort of children with cancer who died between 2014 and 2022, we piloted a rule-based NLP approach to examine the content of clinical notes in the last 6 months of life. We identified documented discussions of five EOLC-QMs: goals of care, limitations to life-sustaining treatments (LLST), hospice, palliative care consultation, and preferred location of death. We assessed performance of NLP methods, compared with gold standard manual chart review. We then used NLP to characterize proportions of decedents with documented EOLC-QM discussions and timing of first documentation relative to death.</p><p><strong>Results: </strong>Among 101 decedents, nearly half were minorities (Hispanic/Latinx [24%], non-Hispanic Black/African American [20%]), female (48%), or diagnosed with solid tumors (43%). Through iterative refinement, our keyword library achieved robust performance statistics (for all EOLC-QMs, F1 score = 1.0). Most decedents had documented discussions regarding goals of care (83%), LLST (83%), and hospice (74%). Fewer decedents had documented discussions regarding palliative care consultation (49%) or preferred location of death (36%). For all five EOLC-QMs, first documentation occurred, on average, >30 days before death.</p><p><strong>Conclusion: </strong>A high proportion of decedents attained specified EOLC-QMs more than 30 days before death. Our findings indicate that NLP is a feasible approach to measuring quality of care for children with cancer at the end of life and is ripe for multi-center research and quality improvement.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400134"},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11407740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing Power in Phase III Oncology Trials With Multivariable Regression: An Empirical Assessment of 535 Primary End Point Analyses.","authors":"Alexander D Sherry, Adina H Passy, Zachary R McCaw, Joseph Abi Jaoude, Timothy A Lin, Ramez Kouzy, Avital M Miller, Gabrielle S Kupferman, Esther J Beck, Pavlos Msaouel, Ethan B Ludmir","doi":"10.1200/CCI.24.00102","DOIUrl":"10.1200/CCI.24.00102","url":null,"abstract":"<p><strong>Purpose: </strong>A previous study demonstrated that power against the (unobserved) true effect for the primary end point (PEP) of most phase III oncology trials is low, suggesting an increased risk of false-negative findings in the field of late-phase oncology. Fitting models with prognostic covariates is a potential solution to improve power; however, the extent to which trials leverage this approach, and its impact on trial interpretation at scale, is unknown. To that end, we hypothesized that phase III trials using multivariable PEP analyses are more likely to demonstrate superiority versus trials with univariable analyses.</p><p><strong>Methods: </strong>PEP analyses were reviewed from trials registered on ClinicalTrials.gov. Adjusted odds ratios (aORs) were calculated by logistic regressions.</p><p><strong>Results: </strong>Of the 535 trials enrolling 454,824 patients, 69% (n = 368) used a multivariable PEP analysis. Trials with multivariable PEP analyses were more likely to demonstrate PEP superiority (57% [209 of 368] <i>v</i> 42% [70 of 167]; aOR, 1.78 [95% CI, 1.18 to 2.72]; <i>P</i> = .007). Among trials with a multivariable PEP model, 16 conditioned on covariates and 352 stratified by covariates. However, 108 (35%) of 312 trials with stratified analyses lost power by categorizing a continuous variable, which was especially common among immunotherapy trials (aOR, 2.45 [95% CI, 1.23 to 4.92]; <i>P</i> = .01).</p><p><strong>Conclusion: </strong>Trials increasing power by fitting multivariable models were more likely to demonstrate PEP superiority than trials with unadjusted analysis. Underutilization of conditioning models and empirical power loss associated with covariate categorization required by stratification were identified as barriers to power gains. These findings underscore the opportunity to increase power in phase III trials with conventional methodology and improve patient access to effective novel therapies.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400102"},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Natural Language Processing Algorithm for Extracting Clinical and Pathological Features of Breast Cancer From Pathology Reports.","authors":"Elisabetta Munzone, Antonio Marra, Federico Comotto, Lorenzo Guercio, Claudia Anna Sangalli, Martina Lo Cascio, Eleonora Pagan, Davide Sangalli, Ilaria Bigoni, Francesca Maria Porta, Marianna D'Ercole, Fabiana Ritorti, Vincenzo Bagnardi, Nicola Fusco, Giuseppe Curigliano","doi":"10.1200/CCI.24.00034","DOIUrl":"https://doi.org/10.1200/CCI.24.00034","url":null,"abstract":"<p><strong>Purpose: </strong>Electronic health records (EHRs) are valuable information repositories that offer insights for enhancing clinical research on breast cancer (BC) using real-world data. The objective of this study was to develop a natural language processing (NLP) model specifically designed to extract structured data from BC pathology reports written in natural language.</p><p><strong>Methods: </strong>During the initial phase, the algorithm's development cohort comprised 193 pathology reports from 116 patients with BC from 2012 to 2016. A rule-based NLP algorithm was applied to extract 26 variables for analysis and was compared with the manual extraction of data performed by both a data entry specialist and an oncologist. Following the first approach, the data set was expanded to include 513 reports, and a Named Entity Recognition (NER)-NLP model was trained and evaluated using K-fold cross-validation.</p><p><strong>Results: </strong>The first approach led to a concordance analysis, which revealed an 82.9% agreement between the algorithm and the oncologist, whereas the concordance between the data entry specialist and the oncologist was 90.8%. The second training approach introduced the definition of an NER-NLP model, in which the accuracy showed remarkable potential (97.8%). Notably, the model demonstrated remarkable performance, especially for parameters such as estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 (F1-score 1.0).</p><p><strong>Conclusion: </strong>The present study aligns with the rapidly evolving field of artificial intelligence (AI) applications in oncology, seeking to expedite the development of complex cancer databases and registries. The results of the model are currently undergoing postprocessing procedures to organize the data into tabular structures, facilitating their utilization in real-world clinical and research endeavors.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400034"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141977162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interinstitutional Approach to Advancing Geospatial Technologies for US Cancer Centers.","authors":"Todd Burus, Josh Martinez, Peter DelNero, Sam Pepper, Isuru Ratnayake, Debora L Oh, Christopher McNair, Hope Krebill, Dinesh Pal Mudaranthakam","doi":"10.1200/CCI.24.00099","DOIUrl":"10.1200/CCI.24.00099","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400099"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11296499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.","authors":"Alaa Albashayreh, Anindita Bandyopadhyay, Nahid Zeinali, Min Zhang, Weiguo Fan, Stephanie Gilbertson White","doi":"10.1200/CCI.23.00235","DOIUrl":"10.1200/CCI.23.00235","url":null,"abstract":"<p><strong>Purpose: </strong>Identifying cancer symptoms in electronic health record (EHR) narratives is feasible with natural language processing (NLP). However, more efficient NLP systems are needed to detect various symptoms and distinguish observed symptoms from negated symptoms and medication-related side effects. We evaluated the accuracy of NLP in (1) detecting 14 symptom groups (ie, pain, fatigue, swelling, depressed mood, anxiety, nausea/vomiting, pruritus, headache, shortness of breath, constipation, numbness/tingling, decreased appetite, impaired memory, disturbed sleep) and (2) distinguishing observed symptoms in EHR narratives among patients with cancer.</p><p><strong>Methods: </strong>We extracted 902,508 notes for 11,784 unique patients diagnosed with cancer and developed a gold standard corpus of 1,112 notes labeled for presence or absence of 14 symptom groups. We trained an embeddings-augmented NLP system integrating human and machine intelligence and conventional machine learning algorithms. NLP metrics were calculated on a gold standard corpus subset for testing.</p><p><strong>Results: </strong>The interannotator agreement for labeling the gold standard corpus was excellent at 92%. The embeddings-augmented NLP model achieved the best performance (F1 score = 0.877). The highest NLP accuracy was observed in pruritus (F1 score = 0.937) while the lowest accuracy was in swelling (F1 score = 0.787). After classifying the entire data set with embeddings-augmented NLP, we found that 41% of the notes included symptom documentation. Pain was the most documented symptom (29% of all notes) while impaired memory was the least documented (0.7% of all notes).</p><p><strong>Conclusion: </strong>We illustrated the feasibility of detecting 14 symptom groups in EHR narratives and showed that an embeddings-augmented NLP system outperforms conventional machine learning algorithms in detecting symptom information and differentiating observed symptoms from negated symptoms and medication-related side effects.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2300235"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insights Into the Patient Experience of Hormone Therapy for Early Breast Cancer Treatment Using Patient Forum Discussions and Natural Language Processing.","authors":"Sameet Sreenivasan, Chao Fang, Emuella M Flood, Natasha Markuzon, Jasmine Y Y Sze","doi":"10.1200/CCI.24.00038","DOIUrl":"10.1200/CCI.24.00038","url":null,"abstract":"<p><strong>Purpose: </strong>Understanding the real-world experience of patients with early breast cancer (eBC) is imperative for optimizing outcomes and evolving patient care. However, there is a lack of patient-level data, hindering clinical development. This social listening study was performed to understand patient insights into symptoms and impacts of hormone therapy (HT) for eBC using posts from patient forums on breastcancer.org to inform future clinical research.</p><p><strong>Methods: </strong>Natural language processing (NLP) and machine learning techniques were used to identify themes related to eBC from a sample of 500,000 posts. After relevant data selection, 362,074 eBC posts were retained for further analysis of symptoms and impacts related to HT, as well as insights into symptom severity, pain locations, and symptom management using exercise and yoga.</p><p><strong>Results: </strong>Overall, 32 symptoms and nine impacts had significant associations with ≥one HT. Hot flush (relative risk [RR], 6.70 [95% CI, 3.36 to 13.36]), arthralgia (RR, 6.67 [95% CI, 3.53 to 12.59]), weight increased (RR, 4.83 [95% CI, 3.20 to 7.28]), mood swings (RR, 7.36 [95% CI, 5.75 to 9.42]), insomnia (RR, 4.76 [95% CI, 3.14 to 7.22]), and depression (RR, 3.05 [95% CI, 1.71 to 5.44]) demonstrated the strongest associations. Severe headache, dizziness, back pain, and muscle spasms showed significant associations with ≥one HT despite their low overall prevalence in eBC posts.</p><p><strong>Conclusion: </strong>The social listening approach allowed the identification of real-world insights from posts specific to eBC HT from a large-scale online breast cancer forum that captured experiences from a uniquely diverse group of patients. Using NLP has a potential to scale analysis of patient feedback and reveal actionable insights into patient experiences of treatment that can inform the development of future therapies and improve the care of patients with eBC.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400038"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer.","authors":"Maria Rosa Salvador Comino, Paul Youssef, Anna Heinzelmann, Florian Bernhardt, Christin Seifert, Mitra Tewes","doi":"10.1200/CCI.24.00041","DOIUrl":"https://doi.org/10.1200/CCI.24.00041","url":null,"abstract":"<p><strong>Purpose: </strong>Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most from palliative care support. We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality.</p><p><strong>Materials and methods: </strong>Between April 1, 2020, and March 31, 2021, a total of 265 patients with advanced cancer completed a patient-reported outcome tool. We documented objective and subjective variables collected from electronic health records, self-reported subjective variables, and all clinical variables combined. We used logistic regression (LR), 20-fold cross-validation, decision trees, and random forests to predict 1-year mortality. We analyzed the receiver operating characteristic (ROC) curve-AUC, the precision-recall curve-AUC (PR-AUC)-and the feature importance of the ML models.</p><p><strong>Results: </strong>The performance of clinical nonpatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predictive than that of subjective patient-reported variables (LR reaches 0.55 [ROC-AUC] and 0.52 [F1 score]).</p><p><strong>Conclusion: </strong>The results show that objective variables used in this study are much more predictive than subjective patient-reported variables, which measure subjective burden. These findings indicate that subjective burden cannot be reliably used to predict survival. Further research is needed to clarify the role of self-reported patient burden and mortality prediction using ML.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400041"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}