Amadeo Jesus Wals Zurita

Purpose: To evaluate whether principal component analysis (PCA) of rectal and bladder dose-volume histograms (DVHs) identifies dose regions associated with late toxicity after prostate stereotactic body radiotherapy (SBRT). Methods/Patients: This retrospective single-institution study included 106 patients with complete linked clinical and dosimetric data after prostate SBRT to 36.25 Gy in five fractions delivered every other day. PCA was applied separately to whole-organ and wall-based rectal and bladder DVHs. Associations with late grade >=2 gastrointestinal (GI) and genitourinary (GU) toxicity at 12 months were explored using Spearman correlation, ROC analysis and deliberately limited logistic regression. Results: Among the 84 patients with available 12-month follow-up in the DVH-linked dosimetric cohort, grade >=2 toxicity was uncommon, with 3 GI events (3.6%) and 2 GU events (2.4%). Intermediate-dose rectal metrics showed the strongest exploratory signal for late GI toxicity, particularly rectal V18.1 Gy (AUC 0.868, 95% CI 0.740-0.997) and V29 Gy (AUC 0.827). These findings should be interpreted cautiously given the low number of events. Rectal wall metrics were less consistent, and no bladder or bladder-wall metric showed clinically meaningful discrimination for GU toxicity. Conclusions: In this low-event SBRT cohort, intermediate whole-rectum dose was associated with late GI toxicity, but the findings are exploratory and require external validation. No isolated dosimetric predictor of late GU toxicity was identified.

Genomic stability maintenance requires the repair of DNA breaks in the most accurate fashion. So, an exquisite regulatory network controls the choice between different repair mechanisms to maximize genome integrity. This relies mostly at the level of DNA end resection, the initial steps of the homologous recombination. On the other hand, numerous cellular activities follow a 24 h oscillation known as the circadian cycle. Thus, we explored the regulation of the choice between different DNA break repair pathways along the circadian cycle. Here we show that in human cells DNA resection shows a circadian oscillation, with a peak at early morning followed by a partial and progressive reduction until late afternoon. Such regulation depends on the circadian clock core component CRY1, which modulates the anti-resection activity of CCAR2 to limit CtIP at nightfall. Additionally, such regulation requires DNA-PK-mediated phosphorylation of CRY1. Finally, this circadian regulation impacts cancer progression and response to radiation therapy of specific tumours.

Objective: We aimed to provide real-world insights into the management of non-small cell lung cancer stage III (NSCLC-SIII) in Spain. Methods: The GOECP-SEOR group conducted an observational, retrospective, multicenter study in which data from patients diagnosed with NSCLC-SIII treated in Spanish radiotherapy departments (RTDs) were collected. Results: A total of 1505 NSCLC-SIII patients from 35 RTDs from 2018 to 2021 were recruited. A total of 871 patients (57.9%) received concurrent chemoradiotherapy (cCRT), and 390 patients (25.91%) received maintenance durvalumab. The immunotherapy (IT) was well tolerated, with Common Terminology Criteria for Adverse Events (CTCAE) Grade 0–2 toxicity observed in 85% of the patients (295 patients). The median overall survival (OS) was 26 months (95% CI 9.4-Not Reached NR). There was a statistically significant difference when patients were stratified by IT, with a median OS of 40 months (95% CI 22.1-NR) in the IT maintenance cohort compared with 19.4 months (95% CI 7.1–58.1) in the non-IT cohort. Differences were also noted in progression-free survival (PFS), with a median PFS of 20.8 months (95% CI 9.6-NR) in the IT cohort versus 8.4 months (95% CI 3.1–25.1) in the non-IT cohort. These differences remained significant according to the multivariate model (OS: HR 0.45 [95% CI 0.36–0.56], p < 0.001; PFS: HR 0.5 [95% CI 0.42–0.59], p < 0.001). Conclusions: The management of NSCLC-SIII patients can vary, leading to differences in treatment outcomes. The treatment of lung cancer in NSCLC-SIII patients in RTDs across Spain aligns with international guidelines and expert recommendations.

Background Immunotherapy in unresectable stage III NSCLC shows delayed treatment effects and time-varying hazards that challenge proportional-hazards assumptions. We developed a continuous-time Markov chain (CTMC) model—with piecewise hazards—to capture the temporal dynamics of durvalumab after chemoradiotherapy (PACIFIC era), enabling transparent extrapolation beyond early trial landmarks. Methods We specified a multi-state CTMC with absorbing death, and clinically interpretable transient states (pre-progression disease control and post-progression). Piecewise transition rates were calibrated to early PACIFIC landmarks (e.g., 0–12–24–36 months) for overall survival (OS) and progression-free survival (PFS), using constrained optimization. External validity was assessed by comparing model-implied OS/PFS trajectories against published PACIFIC follow-ups and large real-world evidence. Uncertainty was quantified via parametric resampling of transition intensities and sensitivity to piecewise knots. Results The CTMC reproduced the hallmark delayed separation of survival curves and the long-tail behavior typical of immune checkpoint inhibition. When calibrated only to early landmarks, the model generated plausible OS and PFS beyond 36 months, approaching 5-year targets reported in PACIFIC follow-ups (e.g., ~43% OS and ~33% PFS with durvalumab), while acknowledging mild underestimation of late PFS in sensitivity scenarios. Goodness-of-fit remained stable across alternative knot placements; uncertainty widened beyond 60 months, as expected from non-individual data calibration. Conclusions A piecewise-hazard CTMC provides a transparent and reproducible framework to model non-proportional, time-dependent immunotherapy effects in stage III NSCLC, reconciling early trial information with clinically coherent long-term projections. This approach can support methodological rigor in survival analysis, scenario planning, and health-economic extrapolation in the PACIFIC setting and similar contexts.

Background: In this study, we evaluate the accuracy, efficiency, and cost-effectiveness of large language models in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. Objective: We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. Methods: We implemented a script using the OpenAI application programming interface to extract structured information in JavaScript object notation format from comorbidities reported in 250 personal history reports. These reports were manually reviewed in batches of 50 by 5 specialists in radiation oncology. We compared the results using metrics such as sensitivity, specificity, precision, accuracy, F-value, κ index, and the McNemar test, in addition to examining the common causes of errors in both humans and generative pretrained transformer (GPT) models. Results: The GPT-3.5 model exhibited slightly lower performance compared to physicians across all metrics, though the differences were not statistically significant (McNemar test, P=.79). GPT-4 demonstrated clear superiority in several key metrics (McNemar test, P<.001). Notably, it achieved a sensitivity of 96.8%, compared to 88.2% for GPT-3.5 and 88.8% for physicians. However, physicians marginally outperformed GPT-4 in precision (97.7% vs 96.8%). GPT-4 showed greater consistency, replicating the exact same results in 76% of the reports across 10 repeated analyses, compared to 59% for GPT-3.5, indicating more stable and reliable performance. Physicians were more likely to miss explicit comorbidities, while the GPT models more frequently inferred nonexplicit comorbidities, sometimes correctly, though this also resulted in more false positives. Conclusions: This study demonstrates that, with well-designed prompts, the large language models examined can match or even surpass medical specialists in extracting information from complex clinical reports. Their superior efficiency in time and costs, along with easy integration with databases, makes them a valuable tool for large-scale data mining and real-world evidence generation.

BACKGROUND In this study, we evaluate the accuracy, efficiency, and cost-effectiveness of large language models in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. OBJECTIVE We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. METHODS We implemented a script using the OpenAI application programming interface to extract structured information in JavaScript object notation format from comorbidities reported in 250 personal history reports. These reports were manually reviewed in batches of 50 by 5 specialists in radiation oncology. We compared the results using metrics such as sensitivity, specificity, precision, accuracy, F-value, κ index, and the McNemar test, in addition to examining the common causes of errors in both humans and generative pretrained transformer (GPT) models. RESULTS The GPT-3.5 model exhibited slightly lower performance compared to physicians across all metrics, though the differences were not statistically significant (McNemar test, P=.79). GPT-4 demonstrated clear superiority in several key metrics (McNemar test, P<.001). Notably, it achieved a sensitivity of 96.8%, compared to 88.2% for GPT-3.5 and 88.8% for physicians. However, physicians marginally outperformed GPT-4 in precision (97.7% vs 96.8%). GPT-4 showed greater consistency, replicating the exact same results in 76% of the reports across 10 repeated analyses, compared to 59% for GPT-3.5, indicating more stable and reliable performance. Physicians were more likely to miss explicit comorbidities, while the GPT models more frequently inferred nonexplicit comorbidities, sometimes correctly, though this also resulted in more false positives. CONCLUSIONS This study demonstrates that, with well-designed prompts, the large language models examined can match or even surpass medical specialists in extracting information from complex clinical reports. Their superior efficiency in time and costs, along with easy integration with databases, makes them a valuable tool for large-scale data mining and real-world evidence generation. CLINICALTRIAL

Introduction: In this study, we evaluate the accuracy, efficiency, and cost-effectiveness of Large Language Models (LLMs) in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. Methods: We implemented a script using the OpenAI API to extract structured information in JSON format from comorbidities reported in 250 personal history reports. These reports were manually reviewed in batches of 50 by five specialists in radiation oncology. We compared the results using metrics such as Sensitivity, Specificity, Precision, Accuracy, F-value, Kappa index, and the McNemar test, in addition to examining the common causes of errors in both humans and GPT models. Results: The GPT-3.5 model exhibited slightly lower performance compared to physicians across all metrics, though the differences were not statistically significant (McNemar’s test p = 0.79). GPT-4 demonstrated clear superiority in several key metrics (McNemar’s test p < 0.001). Notably, it achieved a sensitivity of 96.8%, compared to 88.2% for GPT-3.5 and 88.8% for physicians. However, physicians marginally outperformed GPT-4 in precision (97.7% vs. 96.8%). GPT-4 showed greater consistency, replicating the exact same results in 76% of the reports across 10 repeated analyses, compared to 59% for GPT-3.5, indicating more stable and reliable performance. Physicians were more likely to miss explicit comorbidities, while the GPT models more frequently inferred non-explicit comorbidities, sometimes correctly, though this also resulted in more false positives. Conclusion: This study demonstrates that, with well-designed prompts, the LLMs examined can match or even surpass medical specialists in extracting information from complex clinical reports. Their superior efficiency in time and costs, along with easy integration with databases, makes them a valuable tool for large-scale data mining and real-world evidence generation.

The maintenance of genomic stability is essential for cellular and organismal survival and fitness. Thus, when DNA gets damaged, is essential to repair it in the most accurate fashion. Among different DNA lesions, DNA double strand breaks are specially challenging. An exquisite regulatory network reacts to local and global cues to control the choice between different DNA double strand break repair mechanisms to maximize genomic integrity. Such regulation relies mostly at the level of DNA end resection, the initial steps of the homologous recombination repair pathway. On the other hand, most cellular and organismal activities follow a 24 h oscillation known as the circadian cycle. Such repetitive changes are controlled by an intrinsic, molecular clock built-in at the cellular level which core components are the heterodimers BMAL1-CLOCK and CRY-PER. These inherent rhythms control many different aspects of the cellular metabolism, including the fate of many different DNA transactions. Here we have explored the regulation of the choice between different DNA double strand break repair pathways along the circadian cycle. We observed that DNA end resection shows a circadian oscillation, with a peak at dawn followed by a progressive reduction until dusk. Such regulations depend on the cellular levels of the circadian clock core component CRY1. Consequently, repair by homologous recombination mirrors CRY1 expression levels. Such modulation is controlled through the circadian regulation of the anti-resection activity, but not the protein levels, of CCAR2, that limits CtIP-mediated resection preferentially at nightfall. Additionally, such regulation requires a crosstalk between the DNA damage-dependent phosphorylation of CRY1 by the kinase DNA-PK. Finally, such regulation has an impact in cancer progression and response to radiation therapy of specific tumors. One sentence summary CCAR2-dependent inhibition of DNA end resection and homologous recombination is controlled by the intrinsic cellular circadian clock