Keiji Inoue

This study evaluated changes in blood pressure and hypotension-related adverse drug reactions (ADRs) following oral administration of 5-aminolevulinic acid hydrochloride (5-ALA) in patients undergoing transurethral resection of bladder tumor (TURBT). Photodynamic diagnosis under blue light has demonstrated superior sensitivity compared with that of white light when 5-ALA is administered 4 to 8 h before TURBT (95.3% vs. 61.1%, p < 0.001); however, data regarding associated blood pressure change remain limited. In this study, blood pressure changes were monitored for 24 h after 5-ALA administration (20 mg/kg) and hypotension-related ADRs were assessed within 14 days post-dose. Both systolic and diastolic blood pressures reached their nadir at 2 h after 5-ALA administration. Absolute blood pressure values were lower in patients with ADRs, whereas the magnitude of blood pressure changes was not different between patients with and without ADRs. The incidence of hypotension, defined as systolic blood pressure ≤ 80 mmHg, was 2.1%, which was lower than that reported in previous retrospective studies (40.0–75.6%). Although hypotension-related ADRs occurred in 25.5% of patients, all events were non-serious and resolved without clinical sequelae. The lower incidence may partly reflect the exclusion criteria and medication restrictions applied in this protocol-controlled trial.

Upper tract urothelial carcinoma (UTUC) is a malignant tumor occurring in the urothelial mucosa of the ureter and renal pelvis. Radical nephroureterectomy has been considered the standard of care.^1,2 This procedure has been overused for low-grade tumors with little risk of metastatic progression, and even for some early-stage high-grade superficial tumors,^3,4 leading to increased interest in organ-sparing strategies to preserve renal function. One of the most promising strategies is an endoscopic approach using laser light that can be delivered minimally invasively via an optical fiber and can interact specifically with tumors.⁵ These modalities include photodynamic therapy (PDT)⁶ and laser ablation (LA).⁷ Laser-tissue interactions in these treatments are related to optical absorption and scattering by the tumors, making information on the spatial distribution of light in the tissue crucial for estimating treatment effects.⁸

Background: Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of more than 1 year). When considering the indication for catheter ablation, having a long AF duration is considered a risk factor for recurrence, and therefore, the duration of AF is an important factor in determining the treatment strategy for PeAF. Objective: This study aims to improve the accuracy of the cardiologists’ diagnosis of the AF duration, and the steps to achieve this goal are to develop a predictive model of the AF duration and validate the support performance of the prediction model. Methods: The study included 272 patients with PeAF (aged 20-90 years), with data obtained between January 1, 2015, and December 31, 2023. Of those, 189 (69.5%) were included in the study, excluding 83 (30.5%) who met the exclusion criteria. Of the 189 patients included, 145 (76.7%) were used as training data to build the machine learning (ML) model and 44 (23.3%) were used as test data for predictive ability of the ML model. Using a questionnaire, 10 cardiologists (group A) evaluated whether the test data (44 patients) included AF of more than a 1-year duration (phase 1). Next, the same questionnaire was performed again after providing the ML model’s answer (phase 2). Subsequently, another 10 cardiologists (group B) were shown the test results of group A, were made aware of the limitations of their own diagnostic abilities, and were then administered the same 2-stage test as group A. Results: The prediction results with the ML model using the test data provided 81.8% accuracy (72% sensitivity and 89% specificity). The mean percentage of correct answers in group A was 63.9% (SD 9.6%) for phase 1 and improved to 71.6% (SD 9.3%) for phase 2 (P=.01). The mean percentage of correct answers in group B was 59.8% (SD 5.3%) for phase 1 and improved to 68.2% (SD 5.9%) for phase 2 (P=.007). The mean percentage of answers that differed from the ML model’s prediction for phase 2 (percentage of answers where cardiologists did not trust the ML model and believed their own determination) was 17.3% (SD 10.3%) in group A and 20.9% (SD 5%) in group B and was not significantly different (P=.85). Conclusions: ML models predicting AF duration improved the diagnostic ability of cardiologists. However, cardiologists did not entirely rely on the ML model’s prediction, even if they were aware of their diagnostic capability limitations.

BACKGROUND Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of more than 1 year). When considering the indication for catheter ablation, having a long AF duration is considered a risk factor for recurrence, and therefore, the duration of AF is an important factor in determining the treatment strategy for PeAF. OBJECTIVE This study aims to improve the accuracy of the cardiologists’ diagnosis of the AF duration, and the steps to achieve this goal are to develop a predictive model of the AF duration and validate the support performance of the prediction model. METHODS The study included 272 patients with PeAF (aged 20-90 years), with data obtained between January 1, 2015, and December 31, 2023. Of those, 189 (69.5%) were included in the study, excluding 83 (30.5%) who met the exclusion criteria. Of the 189 patients included, 145 (76.7%) were used as training data to build the machine learning (ML) model and 44 (23.3%) were used as test data for predictive ability of the ML model. Using a questionnaire, 10 cardiologists (group A) evaluated whether the test data (44 patients) included AF of more than a 1-year duration (phase 1). Next, the same questionnaire was performed again after providing the ML model’s answer (phase 2). Subsequently, another 10 cardiologists (group B) were shown the test results of group A, were made aware of the limitations of their own diagnostic abilities, and were then administered the same 2-stage test as group A. RESULTS The prediction results with the ML model using the test data provided 81.8% accuracy (72% sensitivity and 89% specificity). The mean percentage of correct answers in group A was 63.9% (SD 9.6%) for phase 1 and improved to 71.6% (SD 9.3%) for phase 2 (P=.01). The mean percentage of correct answers in group B was 59.8% (SD 5.3%) for phase 1 and improved to 68.2% (SD 5.9%) for phase 2 (P=.007). The mean percentage of answers that differed from the ML model’s prediction for phase 2 (percentage of answers where cardiologists did not trust the ML model and believed their own determination) was 17.3% (SD 10.3%) in group A and 20.9% (SD 5%) in group B and was not significantly different (P=.85). CONCLUSIONS ML models predicting AF duration improved the diagnostic ability of cardiologists. However, cardiologists did not entirely rely on the ML model’s prediction, even if they were aware of their diagnostic capability limitations.

Bladder cancer (BC) possesses distinct molecular profiles that influence progression depending on its biological nature and delivered treatment intensity. Muscle-invasive BC (MIBC) and non-MIBC (NMIBC) demonstrate great intrinsic heterogeneity regarding different prognoses, survival, progression, and treatment outcomes. Transurethral resection of bladder tumor (TURBT) is the standard of care in treating NMIBC and serves both diagnostic and therapeutic purposes despite the prevalent recurrence and progression among many patients. In particular, flat urothelial carcinoma in situ and urothelial carcinoma with lamina propria invasion are the major precursors of MIBC. A new-generation photosensitizer, 5-Aminolevulinic acid (5-ALA), demonstrates high tumor specificity by illuminating the tumor lesion with a specific wavelength of light to produce fluorescence and has been studied for photodynamic diagnosis to detect precise tumor areas by TURBT. Additionally, it has been applied for treatment by producing its cytotoxic reactive oxygen species, as well as screening for urological carcinomas by excreting porphyrin in the blood and urine. Moreover, 5-ALA may contribute to screening before and after TURBT in NMIBC. Here, we summarize the updated evidence and ongoing research on photodynamic technology for NMIBC, providing insight into the potential for improving patient outcomes.

Bladder cancer (BC) possesses distinct molecular profiles that influence progression depending on its biological nature and delivered treatment intensity. Muscle-invasive BC (MIBC) and non-MIBC (NMIBC) demonstrate great intrinsic heterogeneity regarding different prognoses, survival, progression, and treatment outcomes. Transurethral resection of bladder tumor (TURBT) is the standard of care in treating NMIBC and serves both diagnostic and therapeutic purposes, despite the prevalent recurrence and progression among many patients. In particular, flat urothelial car-cinoma in situ and urothelial carcinoma with lamina propria invasion are the major precursors of MIBC. A new‐generation photosensitizer, 5‐Aminolevulinic acid (5-ALA), demonstrates high tumor specificity by illuminating the tumor lesion with a specific wavelength of light to produce fluorescence, and has been studied for photodynamic diagnosis to detect precise tumor areas by TURBT. Additionally, it has been applied for treatment by producing its cytotoxic reactive oxygen species, as well as screening for urological carcinomas by excreting porphyrin in the blood and urine. Moreover, 5-ALA may contribute to screening before and after TURBT in NMIBC. Here, we summarize the updated evidence and ongoing research on photodynamic technology for NMIBC, providing insight into the potential for improving patient outcomes.