JCO Clinical Cancer Informatics最新文献

{"title":"Automated Extraction of Patient-Centered Outcomes After Breast Cancer Treatment: An Open-Source Large Language Model-Based Toolkit.","authors":"Man Luo, Shubham Trivedi, Allison W Kurian, Kevin Ward, Theresa H M Keegan, Daniel Rubin, Imon Banerjee","doi":"10.1200/CCI.23.00258","DOIUrl":"10.1200/CCI.23.00258","url":null,"abstract":"<p><strong>Purpose: </strong>Patient-centered outcomes (PCOs) are pivotal in cancer treatment, as they directly reflect patients' quality of life. Although multiple studies suggest that factors affecting breast cancer-related morbidity and survival are influenced by treatment side effects and adherence to long-term treatment, such data are generally only available on a smaller scale or from a single center. The primary challenge with collecting these data is that the outcomes are captured as free text in clinical narratives written by clinicians.</p><p><strong>Materials and methods: </strong>Given the complexity of PCO documentation in these narratives, computerized methods are necessary to unlock the wealth of information buried in unstructured text notes that often document PCOs. Inspired by the success of large language models (LLMs), we examined the adaptability of three LLMs, GPT-2, BioGPT, and PMC-LLaMA, on PCO tasks across three institutions, Mayo Clinic, Emory University Hospital, and Stanford University. We developed an open-source framework for fine-tuning LLM that can directly extract the five different categories of PCO from the clinic notes.</p><p><strong>Results: </strong>We found that these LLMs without fine-tuning (zero-shot) struggle with challenging PCO extraction tasks, displaying almost random performance, even with some task-specific examples (few-shot learning). The performance of our fine-tuned, task-specific models is notably superior compared with their non-fine-tuned LLM models. Moreover, the fine-tuned GPT-2 model has demonstrated a significantly better performance than the other two larger LLMs.</p><p><strong>Conclusion: </strong>Our discovery indicates that although LLMs serve as effective general-purpose models for tasks across various domains, they require fine-tuning when applied to the clinician domain. Our proposed approach has the potential to lead more efficient, adaptable models for PCO information extraction, reducing reliance on extensive computational resources while still delivering superior performance for specific tasks.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2300258"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11867221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019543","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":"Evaluation of Real-World Tumor Response Derived From Electronic Health Record Data Sources: A Feasibility Analysis in Patients With Metastatic Non-Small Cell Lung Cancer Treated With Chemotherapy.","authors":"Brittany A McKelvey, Elizabeth Garrett-Mayer, Donna R Rivera, Amy Alabaster, Hillary S Andrews, Elizabeth G Bond, Thomas D Brown, Amanda Bruno, Lauren Damato, Janet L Espirito, Laura L Fernandes, Eric Hansen, Paul Kluetz, Xinran Ma, Andrea McCracken, Pallavi S Mishra-Kalyani, Yanina Natanzon, Danielle Potter, Nicholas J Robert, Lawrence Schwartz, Regina Schwind, Connor Sweetnam, Joseph Wagner, Mark D Stewart, Jeff D Allen","doi":"10.1200/CCI.24.00091","DOIUrl":"10.1200/CCI.24.00091","url":null,"abstract":"<p><strong>Purpose: </strong>Real-world data (RWD) holds promise for ascribing a real-world (rw) outcome to a drug intervention; however, ascertaining rw-response to treatment from RWD can be challenging. Friends of Cancer Research formed a collaboration to assess available data attributes related to rw-response across RWD sources to inform methods for capturing, defining, and evaluating rw-response.</p><p><strong>Materials and methods: </strong>This retrospective noninterventional (observational) study included seven electronic health record data companies (data providers) providing summary-level deidentified data from 200 patients diagnosed with metastatic non-small cell lung cancer (mNSCLC) and treated with first-line platinum doublet chemotherapy following a common protocol. Data providers reviewed the availability and frequency of data components to assess rw-response (ie, images, radiology imaging reports, and clinician response assessments). A common protocol was used to assess and report rw-response end points, including rw-response rate (rwRR), rw-duration of response (rwDOR), and the association of rw-response with rw-overall survival (rwOS), rw-time to treatment discontinuation (rwTTD), and rw-time to next treatment (rwTTNT).</p><p><strong>Results: </strong>The availability and timing of clinician assessments was relatively consistent across data sets in contrast to images and image reports. Real-world response was analyzed using clinician response assessments (median proportion of patients evaluable, 77.5%), which had the highest consistency in the timing of assessments. Relative consistency was observed across data sets for rwRR (median 46.5%), as well as the median and directionality of rwOS, rwTTD, and rwTTNT. There was variability in rwDOR across data sets.</p><p><strong>Conclusion: </strong>This collaborative effort demonstrated the feasibility of aligning disparate data sources to evaluate rw-response end points using clinician-documented responses in patients with mNSCLC. Heterogeneity exists in the availability of data components to assess response and related rw-end points, and further work is needed to inform drug effectiveness evaluation within RWD sources.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400091"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989449","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":"<i>Cureit</i>: An End-to-End Pipeline for Implementing Mixture Cure Models With an Application to Liposarcoma Data.","authors":"Karissa Whiting, Teng Fei, Samuel Singer, Li-Xuan Qin","doi":"10.1200/CCI.23.00234","DOIUrl":"10.1200/CCI.23.00234","url":null,"abstract":"<p><strong>Purpose: </strong>Cure models are a useful alternative to Cox proportional hazards models in oncology studies when there is a subpopulation of patients who will not experience the event of interest. Although software is available to fit cure models, there are limited tools to evaluate, report, and visualize model results. This article introduces the <i>cureit</i> R package, an end-to-end pipeline for building mixture cure models, and demonstrates its use in a data set of patients with primary extremity and truncal liposarcoma.</p><p><strong>Methods: </strong>To assess associations between liposarcoma histologic subtypes and disease-specific death (DSD) in patients treated at Memorial Sloan Kettering Cancer Center between July 1982 and September 2017, mixture cure models were fit and evaluated using the <i>cureit</i> package. Liposarcoma histologic subtypes were defined as well-differentiated, dedifferentiated, myxoid, round cell, and pleomorphic.</p><p><strong>Results: </strong>All other analyzed liposarcoma histologic subtypes were significantly associated with higher DSD in cure models compared with well-differentiated. In multivariable models, myxoid (odds ratio [OR], 6.25 [95% CI, 1.32 to 29.6]) and round cell (OR, 16.2 [95% CI, 2.80 to 93.2]) liposarcoma had higher incidences of DSD compared with well-differentiated patients. By contrast, dedifferentiated liposarcoma was associated with the latency of DSD (hazard ratio, 10.6 [95% CI, 1.48 to 75.9]). Pleomorphic liposarcomas had significantly higher risk in both incidence and the latency of DSD (<i>P</i> < .0001). Brier scores indicated comparable predictive accuracy between cure and Cox models.</p><p><strong>Conclusion: </strong>We developed the <i>cureit</i> pipeline to fit and evaluate mixture cure models and demonstrated its clinical utility in the liposarcoma disease setting, shedding insights on the subtype-specific associations with incidence and/or latency.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2300234"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141879769","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-Transfer-Learning-Based Natural Language Processing of Serial Free-Text Computed Tomography Reports for Predicting Survival of Patients With Pancreatic Cancer.","authors":"Sunkyu Kim, Seung-Seob Kim, Eejung Kim, Michael Cecchini, Mi-Suk Park, Ji A Choi, Sung Hyun Kim, Ho Kyoung Hwang, Chang Moo Kang, Hye Jin Choi, Sang Joon Shin, Jaewoo Kang, Choong-Kun Lee","doi":"10.1200/CCI.24.00021","DOIUrl":"https://doi.org/10.1200/CCI.24.00021","url":null,"abstract":"<p><strong>Purpose: </strong>To explore the predictive potential of serial computed tomography (CT) radiology reports for pancreatic cancer survival using natural language processing (NLP).</p><p><strong>Methods: </strong>Deep-transfer-learning-based NLP models were retrospectively trained and tested with serial, free-text CT reports, and survival information of consecutive patients diagnosed with pancreatic cancer in a Korean tertiary hospital was extracted. Randomly selected patients with pancreatic cancer and their serial CT reports from an independent tertiary hospital in the United States were included in the external testing data set. The concordance index (c-index) of predicted survival and actual survival, and area under the receiver operating characteristic curve (AUROC) for predicting 1-year survival were calculated.</p><p><strong>Results: </strong>Between January 2004 and June 2021, 2,677 patients with 12,255 CT reports and 670 patients with 3,058 CT reports were allocated to training and internal testing data sets, respectively. ClinicalBERT (Bidirectional Encoder Representations from Transformers) model trained on the single, first CT reports showed a c-index of 0.653 and AUROC of 0.722 in predicting the overall survival of patients with pancreatic cancer. ClinicalBERT trained on up to 15 consecutive reports from the initial report showed an improved c-index of 0.811 and AUROC of 0.911. On the external testing set with 273 patients with 1,947 CT reports, the AUROC was 0.888, indicating the generalizability of our model. Further analyses showed our model's contextual interpretation beyond specific phrases.</p><p><strong>Conclusion: </strong>Deep-transfer-learning-based NLP model of serial CT reports can predict the survival of patients with pancreatic cancer. Clinical decisions can be supported by the developed model, with survival information extracted solely from serial radiology reports.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400021"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992497","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":"The More, the Better? Modalities of Metastatic Status Extraction on Free Medical Reports Based on Natural Language Processing.","authors":"Emmanuelle Kempf, Sonia Priou, Ariel Cohen, Akram Redjdal, Etienne Guével, Xavier Tannier","doi":"10.1200/CCI.24.00026","DOIUrl":"https://doi.org/10.1200/CCI.24.00026","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400026"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074533","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":"Prostate-Specific Antigen Screening and Prostate Cancer Mortality: An Emulation of Target Trials in US Medicare.","authors":"Xabier García-Albéniz, John Hsu, Ruth Etzioni, June M Chan, Joy Shi, Barbra Dickerman, Miguel A Hernán","doi":"10.1200/CCI.24.00094","DOIUrl":"10.1200/CCI.24.00094","url":null,"abstract":"<p><strong>Purpose: </strong>No consensus about the effectiveness of prostate-specific antigen (PSA) screening exists among clinical guidelines, especially for the elderly. Randomized trials of PSA screening have yielded different results, partly because of variations in adherence, and it is unlikely that new trials will be conducted. Our objective was to estimate the effect of annual PSA screening on prostate cancer (PC) mortality in Medicare beneficiaries age 67-84 years.</p><p><strong>Methods: </strong>This is a large-scale, population-based, observational study of two screening strategies: annual PSA screening and no screening. We used data from 537,599 US Medicare (2001-2008) beneficiaries age 67-84 years who had a good life expectancy, no previous PC, and no PSA test in the 2 years before baseline. We estimated the 8-year PC mortality and incidence, treatments for PC, and treatment complications of PSA screening.</p><p><strong>Results: </strong>In men age 67-74 years, the estimated difference in 8-year risk of PC death between PSA screening and no screening was -2.3 (95% CI, -4.1 to -1.1) deaths per 1,000 men (a negative risk difference favors screening). Treatment complications were more frequent under PSA screening than under no screening. In men age 75-84 years, risk difference estimates were closer to zero.</p><p><strong>Conclusion: </strong>Our estimates suggest that under conventional statistical criteria, annual PSA screening for 8 years is highly compatible with reductions of PC mortality from four to one fewer PC deaths per 1,000 screened men age 67-74 years. As with any study using real-world data, the estimates could be affected by residual confounding.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400094"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005809","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":"Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the <i>All of Us</i> Research Program.","authors":"Lew Berman, Yechiam Ostchega, John Giannini, Lakshmi Priya Anandan, Emily Clark, Matthew Spotnitz, Lina Sulieman, Michael Volynski, Andrea Ramirez","doi":"10.1200/CCI.24.00052","DOIUrl":"https://doi.org/10.1200/CCI.24.00052","url":null,"abstract":"<p><strong>Purpose: </strong>The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using <i>All of Us</i> Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework.</p><p><strong>Methods: </strong>We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older.</p><p><strong>Results: </strong>Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality.</p><p><strong>Conclusion: </strong>This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the <i>All of Us</i> Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400052"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044174","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":"Clinical Calculator for Predicting Freedom From Recurrence After Resection of Stage I-III Colon Cancer in Patients With Microsatellite Instability.","authors":"Ayyuce Begum Bektas, Lynn Hakki, Asama Khan, Maria Widmar, Iris H Wei, Emmanouil Pappou, J Joshua Smith, Garrett M Nash, Philip B Paty, Julio Garcia-Aguilar, Andrea Cercek, Zsofia Stadler, Neil H Segal, Jinru Shia, Mithat Gonen, Martin R Weiser","doi":"10.1200/CCI.23.00233","DOIUrl":"10.1200/CCI.23.00233","url":null,"abstract":"<p><strong>Purpose: </strong>Outcome for patients with nonmetastatic, microsatellite instability (MSI) colon cancer is favorable: however, high-risk cohorts exist. This study was aimed at developing and validating a nomogram model to predict freedom from recurrence (FFR) for patients with resected MSI colon cancer.</p><p><strong>Patients and methods: </strong>Data from patients who underwent curative resection of stage I, II, or III MSI colon cancer in 2014-2021 (model training cohort, 384 patients, 33 events; median follow-up, 38.8 months) were retrospectively collected from institutional databases. Variables associated with recurrence in multivariable analysis were selected for inclusion in the clinical calculator. The calculator's predictive accuracy was measured with the concordance index and validated using data from patients who underwent treatment for MSI colon cancer in 2007-2013 (validation cohort, 164 patients, eight events; median follow-up, 84.8 months).</p><p><strong>Results: </strong>T category and number of positive lymph nodes were significantly associated with recurrence in multivariable analysis and were selected for inclusion in the clinical calculator. The calculator's concordance index for FFR in the model training cohort was 0.812 (95% CI, 0.742 to 0.873), compared with 0.759 (95% CI, 0.683 to 0.840) for the staging schema of the eighth edition of the American Joint Committee on Cancer Staging Manual. The concordance index for the validation cohort was 0.744 (95% CI, 0.666 to 0.822), confirming robust predictive accuracy.</p><p><strong>Conclusion: </strong>Although in general patients with nonmetastatic MSI colon cancer had favorable outcome, patients with advanced T category and multiple metastatic lymph nodes had higher risk of recurrence. The clinical calculator identified patients with MSI colon cancer at high risk for recurrence, and this could inform surveillance strategies. In addition, the model could be used in trial design to identify patients suitable for novel adjuvant therapy.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2300233"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11323037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910173","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":"Classification and Regression Trees to Predict for Survival for Patients With Hepatocellular Carcinoma Treated With Atezolizumab and Bevacizumab.","authors":"Timothy J Brown, Phyllis A Gimotty, Ronac Mamtani, Thomas B Karasic, Yu-Xiao Yang","doi":"10.1200/CCI.23.00220","DOIUrl":"10.1200/CCI.23.00220","url":null,"abstract":"<p><strong>Purpose: </strong>Systemic therapy with atezolizumab and bevacizumab can extend life for patients with advanced hepatocellular carcinoma (HCC). However, there is substantial variability in response to therapy and overall survival. Although current prognostic models have been validated in HCC, they primarily consider covariates that may be reflective of the severity of the underlying liver disease of patients with HCC. We developed and internally validated a classification and regression tree (CART) to identify patient characteristics associated with risks of early mortality, at or before 6 months from treatment initiation.</p><p><strong>Methods: </strong>This retrospective cohort study used the nationwide Flatiron Health electronic health record-derived deidentified database and included patients with a diagnosis of HCC after January 1, 2020, who received initial systemic therapy with atezolizumab and bevacizumab. CART was developed from available baseline clinical and demographic information to predict mortality within 6 months from treatment initiation. Model characteristics were compared to the albumin-bilirubin (ALBI) model and was further validated against a contemporary validation cohort of patients after a data update.</p><p><strong>Results: </strong>A total of 293 patients were analyzed. The CART identified seven cohorts of patients from baseline demographic and laboratory characteristics. The model had an area under the receiver operating curve (AUROC) of 0.739 (95% CI, 0.683 to 0.794) for predicting 6-month mortality. This model was internally valid and performed more favorably than the ALBI model, which had an AUROC of 0.608 (95% CI, 0.557 to 0.660). The model applied to the contemporary validation cohort (n = 111) had an AUROC of 0.666 (95% CI, 0.506 to 0.826).</p><p><strong>Conclusion: </strong>Using CART, we identified unique cohorts of patients with HCC treated with atezolizumab and bevacizumab with distinct risks of early mortality. This approach outperformed the ALBI model and used clinical and laboratory characteristics that are readily available to oncologists caring for these patients.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2300220"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11296500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876704","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":"Emergence of Digital Toxicity and the Need for an Integrated, Patient-Centric Approach to the Development, Evaluation, and Use of Digital Health Tools for Oncology.","authors":"Chris Gibbons, Carole Baas, Caroline Chung","doi":"10.1200/CCI.23.00105","DOIUrl":"10.1200/CCI.23.00105","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2300105"},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898874","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}