JCO Clinical Cancer Informatics最新文献

Purpose: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Purpose: To explore the predictive potential of serial computed tomography (CT) radiology reports for pancreatic cancer survival using natural language processing (NLP).

Methods: 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.

Results: 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.

Conclusion: 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.

Purpose: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Purpose: 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.

Patients and methods: 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).

Results: 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.

Conclusion: 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.

Purpose: 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 All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework.

Methods: 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.

Results: 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.

Conclusion: This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us 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.

Purpose: Patient outcomes may differ from randomized trial averages. We aimed to predict benefit from FOLFOXIRI versus infusional fluorouracil, leucovorin, and oxaliplatin/fluorouracil, leucovorin, and irinotecan (FOLFOX/FOLFIRI), both plus bevacizumab, in patients with metastatic colorectal cancer (mCRC).

Methods: A Cox model with prespecified clinical, molecular, and laboratory variables was developed in 639 patients from the TRIBE2 trial for predicting 2-year mortality. Data from the CHARTA (n = 232), TRIBE1 (n = 504), and CAIRO5 (liver-only mCRC, n = 287) trials were used for external validation and heterogeneity of treatment effects (HTE) analysis. This involves categorizing patients into risk groups and assessing treatment effects across these groups. Performance was assessed by the C-index and calibration plots. The C-for-benefit was calculated to assess evidence for HTE. The c-for-benefit is specifically designed for HTE analysis. Like the commonly known c-statistic, it summarizes the discrimination of a model. Values over 0.5 indicate evidence for HTE.

Results: In TRIBE2, the overoptimism-corrected C-index was 0.66 (95% CI, 0.63 to 0.69). At external validation, the C-index was 0.69 (95% CI, 0.64 to 0.75), 0.68 (95% CI, 0.64 to 0.72), and 0.65 (95% CI, 0.65 to 0.66), in CHARTA, TRIBE1, and CAIRO5, respectively. Calibration plots indicated slight underestimation of mortality. The c-for-benefit indicated evidence for HTE in CHARTA (0.56, 95% CI, 0.48 to 0.65), but not in TRIBE1 (0.49, 95% CI, 0.44 to 0.55) and CAIRO5 (0.40, 95% CI, 0.32 to 0.48).

Conclusion: Although 2-year mortality could be reasonably estimated, the HTE analysis showed that clinically available variables did not reliably identify which patients with mCRC benefit from FOLFOXIRI versus FOLFOX/FOLFIRI, both plus bevacizumab, across the three studies.

Purpose: Contrast enhancement is necessary for visualizing, diagnosing, and treating brain tumors. Through this study, we aimed to examine the potential role of general adversarial neural networks in generating artificial intelligence-based enhancement of tumors using a lightweight model.

Patients and methods: A retrospective study was conducted on magnetic resonance imaging scans of patients diagnosed with brain tumors between 2020 and 2023. A generative adversarial neural network was built to generate images that would mimic the real contrast enhancement of these tumors. The performance of the neural network was evaluated quantitatively by VGG-16, ResNet, binary cross-entropy loss, mean absolute error, mean squared error, and structural similarity index measures. Regarding the qualitative evaluation, nine cases were randomly selected from the test set and were used to build a short satisfaction survey for experienced medical professionals.

Results: One hundred twenty-nine patients with 156 scans were identified from the hospital database. The data were randomly split into a training set and validation set (90%) and a test set (10%). The VGG loss function for training, validation, and test sets were 2,049.8, 2,632.6, and 4,276.9, respectively. Additionally, the structural similarity index measured 0.366, 0.356, and 0.3192, respectively. At the time of submitting the article, 23 medical professionals responded to the survey. The median overall satisfaction score was 7 of 10.

Conclusion: Our network would open the door for using lightweight models in performing artificial contrast enhancement. Further research is necessary in this field to reach the point of clinical practicality.