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Publications

Joseph C. Liao

Kathryn Simmons Stamey Professor

Publications

    • CystoDS: a multiclass endoscopy image dataset for artificial intelligence-assisted bladder cancer detection.

      Lee, T. J., Qiu, L., Long, J., Mach, K. E., Peterson, D., Yao, Q., Shkolyar, E., Islam, M. T., Xing, L., Liao, J. C.

      Scientific data

      ABSTRACT
      Cystoscopy is a common endoscopic procedure used for the visual inspection of the lower urinary tract, particularly for the detection and surveillance of bladder cancer. Artificial intelligence (AI) strategies could help address the recognized shortcomings of cystoscopy by identifying malignant and non-malignant regions of interest (ROIs) and providing real-time clinical decision support for biopsy and surgical resection. However, curating a dataset for training AI models is challenging and time-time consuming. We present CystoDS, a high-quality bladder imaging dataset derived from standard white light cystoscopy that is ready for AI applications for detection of bladder cancer and cancer-mimicking benign lesions. This dataset includes 8,067 images from 160 patients labelled with five classes and 22 subclasses, along with segmentation data for 768 of the images. We detail the methods used for image acquisition, the structure of the dataset, and our technical validation process to demonstrate the AI-readiness of the data.
      https://www.ncbi.nlm.nih.gov/pubmed/41741508 https://doi.org/10.1038/s41597-026-06887-z
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    • Field-effect-informed urine liquid biopsy for bladder cancer.

      Shi, W. Y., Liu, K. J., Esfahani, M. S., Mach, K. E., Phillips, N. A., Almanza, D., Bajpai, R. K., Schroers-Martin, J. G., Trabanino, L., Lee, T. J., La, V., Rodriguez, G., Holton, G., Chen, S. B., Mullane, P., Wu, D. J., Nesselbush, M. C., Sugio, T., Cheng, J. C., Jabara, I., Hamilton, E. G., Alig, S. K., Liu, C. L., Peterson, D. J., Prado, K., Shkolyar, E., Thong, A., Shah, J. B., Gill, H., Kunder, C. A., Chan, E., Khaki, A. R., Skinner, E. C., Alizadeh, A. A., Liao, J. C., Diehn, M.

      Cell

      ABSTRACT
      Only some non-muscle-invasive bladder cancer (NMIBC) patients benefit from intravesical Bacillus Calmette-Guérin (BCG), and predictive biomarkers remain lacking. While urine tumor DNA (utDNA) analysis is promising, mutations in tumor-adjacent normal urothelium, namely the field effect, limit specificity. Here, we show that the prevalence of somatic mutations in the urine increases with age. We introduce an improved utDNA minimal residual disease (MRD) approach that increases specificity by removing field-effect mutations. Applying this field-effect-informed MRD approach to 261 samples from NMIBC patients undergoing surgery and adjuvant BCG, we identify three molecular response classes, including surgical responders, BCG responders, and non-responders. Molecular predictors of response to the two treatments differ, with pre-existing immune activation and higher mutation burden enriched in BCG but not surgery responders. These findings highlight the potential of field-effect-informed liquid biopsy methods for guiding personalized therapy and uncovering biomarkers for individual components of multimodal treatments.
      https://www.ncbi.nlm.nih.gov/pubmed/41605210 https://doi.org/10.1016/j.cell.2025.12.054
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    • Magnetic retrieval of kidney stones via ureteroscopy in a porcine model

      Roquero, D., Ge, T., Holton, G. H., Kornberg, Z., Bryan, J. T., Doeve, J., Lau, H., Sheth, K., Conti, S., Mach, K. E., Wang, S. X., Liao, J. C.

      DEVICE

      https://doi.org/10.1016/j.device.2025.100971
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    • Performance of the EORTC and CUETO risk prediction models in contemporary patients undergoing transurethral resection of bladder tumor with blue light cystoscopy.

      Perez-Londoño, A., Kaul, S., Fleishman, A., Konety, B., Pohar, K., Holzbeierlein, J. M., Taylor, J., Kates, M., Willard, B., Taylor, J. M., Liao, J. C., Kaimakliotis, H. Z., Porten, S. P., Steinberg, G. D., Tyson, M. D., Lotan, Y., Daneshmand, S., Gershman, B.

      Urologic oncology

      ABSTRACT
      The European Organization for Research and Treatment of Cancer (EORTC) and the Club Urológico Español de Tratamiento Oncológico (CUETO) risk prediction models are established tools for predicting recurrence and progression in non-muscle invasive bladder cancer. However, their performance in patients undergoing TURBT with blue light cystoscopy (BLC-TURBT), which reduces disease recurrence, has not been examined. We, therefore, evaluated the performance of these models in a contemporary cohort of patients undergoing BLC-TURBT.We identified patients aged 18 to 89 years in the multi-institutional Blue Light Cystoscopy with Cysview Registry who underwent BLC-TURBT between 2014 and 2023. We predicted disease recurrence- and progression-free survival estimates using the EORTC and CUETO models, and estimated observed recurrence-free (RFS) and progression-free (PFS) survival using the Kaplan-Meier method.A total of 899 patients were included. Tumor stage was Ta in 658 (73%) and T1 in 241 (27%) patients. EORTC risk score groups were associated with a statistically significant difference in RFS, whereas CUETO risk score groups were not. Observed RFS and PFS estimates were generally higher than that predicted by the EORTC risk score, while observed RFS and PFS were inconsistently related to that predicted by the CUETO risk score. The c-statistics for RFS and PFS by risk score group were 0.59 and 0.67 for EORTC, and 0.53 and 0.72 for CUETO, respectively.In a contemporary cohort of patients undergoing BLC-TURBT, the EORTC and CUETO models demonstrated poor performance in predicting RFS and PFS. These results underscore the need for development of more accurate predictive models to guide the risk-adapted management of patients undergoing BLC-TURBT.
      https://www.ncbi.nlm.nih.gov/pubmed/42744708 https://doi.org/10.1016/j.urolonc.2026.08.005
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    • Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer.

      Kim, I. E., Meier-Scherling, C. P., Zhou, S. R., Soerensen, S. J., Ajjawi, I., Chung, B. I., Shkolyar, E., Sonn, G. A., Liao, J. C., Leapman, M. S.

      Urologic oncology

      ABSTRACT
      OBJECTIVE: To evaluate the performance and interpretability of multiple ML algorithms for survival prediction in prostate cancer (CaP) and to assess whether these approaches can improve upon established clinical risk prediction models.METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) 17 Database, we identified patients diagnosed with CaP from 2010 to 2017 and implemented a Bayesian Cox variable selection model to determine the most clinically relevant features. To predict overall survival (OS) and prostate cancer-specific survival (PCSS), we applied these features to train and test a traditional Cox proportional hazards (PH) model in comparison to ML models including XGBoost, random survival forests, and elastic nets as well as the Cancer of the Prostate Risk Assessment (CAPRA) score using 5-fold cross-validation. The models were repeated for cohorts limited to patients who underwent radical prostatectomy and those who had low-risk disease.RESULTS: For both OS and PCSS, XGBoost had the best area under the curve (AUC), concordance index, and Brier score, followed by elastic net, Cox PH, and RSFs. Feature analysis indicated that age was most predictive of OS, while cancer-specific characteristics such as Gleason grade group were the most important features for PCSS prediction.CONCLUSION: While the traditional Cox PH model remained a clinically robust cancer survival model, this study found that ML models demonstrated modestly improved CaP survival prediction and provided easily interpretable information on the factors most important for prediction. These findings underscore the ability of new ML algorithms to be clinically useful in predicting survival in patients with CaP, specifically by identifying high-risk patients who may require more intensive treatment plans and closer follow-up.
      https://www.ncbi.nlm.nih.gov/pubmed/42674923 https://doi.org/10.1016/j.urolonc.2026.07.015
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    • Performance of Urine Tumor DNA Detection in Patients with High-grade Non-muscle-invasive Bladder Cancer Undergoing Repeat Transurethral Resection.

      Ogunkeye, J., Shkolyar, E., Mach, K. E., Prendiville, S., Prado, K., Shah, J. B., Skinner, E. C., Fischer, D. S., Phillips, K. G., Bicocca, V. T., Levin, T. G., Liao, J. C.

      European urology focus

      ABSTRACT
      Patients with high-risk non-muscle invasive bladder cancer (NMIBC) experience high recurrence rates and potential understaging, supporting guideline recommendations for repeat transurethral resection (reTUR). However, identifying patients best suited for reTUR remains challenging. Here, we analyze urine tumor DNA (utDNA) for mutations associated with urothelial cancer in a cohort of patients undergoing reTUR.Retrospective evaluation of urine samples from 43 patients with pathologically confirmed high-grade NMIBC who were undergoing reTUR. Urine was collected immediately prior to reTUR and evaluated using UroAmp. The primary objective of this study was to compare utDNA disease classification with pathological staging from reTUR. Prognostic (high/low-risk) and minimal residual disease (MRD) (positive/negative) classifications were evaluated.The stage distribution from reTUR was 32% T0 (no residual disease), 29% Ta ±CIS, 28% T1 ±CIS, and 11% T2. Prognostic risk classification had 97% sensitivity (95% confidence interval [CI] 86-100) and 89% negative predictive value (95% CI 59-99). MRD classification had 80% specificity (95% CI 56-94) and 88% positive predictive value (95% CI 72-97). When long-term recurrence outcomes were considered, positive predictive value increased to 92% (95% CI 78-98) for both classification algorithms. Among T0 samples, utDNA high-risk patients had recurrence-free survival of 36%, compared to 100% for utDNA low-risk patients (p = 0.006).utDNA testing identifies residual disease prior to reTUR. utDNA can serve as an adjunct following initial transurethral resection, aiding in the assessment of resection completeness, determining the need and timing of repeat resection, and stratifying risk of future recurrence.
      https://www.ncbi.nlm.nih.gov/pubmed/42538173 https://doi.org/10.1016/j.euf.2026.06.002
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    • Large multicenter validation of urine RNA profile for urothelial carcinoma detection and surveillance.

      Mach, K. E., Kornberg, Z., Shkolyar, E., Long, J., Lee, T. J., La, V., Yoo, I., Rodriguez, G., Thong, A. E., Prado, K. B., Shah, J. B., Leppert, J. T., Skinner, E. C., Liao, J. C.

      The Journal of clinical investigation

      ABSTRACT
      Current diagnosis and surveillance of bladder cancer relies on cystoscopy which is invasive and user dependent. The urine mRNA panel, uRNAp, measures expression of 3 genes for identification of bladder cancer. Here we report validation of uRNAp for patients undergoing initial work-up for suspected bladder cancer and surveillance for bladder cancer.Urine specimens were prospectively collected prior to cystoscopy at two health care systems from patients without (detection cohort) or with (surveillance cohort) a history of bladder cancer. RNA was isolated from urine sediment for RT-qPCR to determine ROBO1, CRH, and IGF2 expression and calculate the uRNAp bladder cancer probability score.In the detection cohort, 547 samples were collected from 529 patients. There were 123 new diagnoses of bladder cancer in the detection cohort and uRNAp demonstrated 98% sensitivity and 51% specificity for identification of bladder cancer. In the surveillance cohort, 1543 samples were collected from 447 patients with 286 recurrences. uRNAp demonstrated 94% overall sensitivity with 43% specificity and 99% sensitivity for high-grade recurrence. The receiver operating characteristic area under the curve was 0.92 in the detection and 0.81 in the surveillance cohort. uRNAp scores significantly increased with tumor size and grade.Prospective validation of uRNAp demonstrated a strong potential clinical utility as a non-invasive adjunct to cystoscopy for management of bladder cancer. uRNAp may be a useful triage tool to defer or expedite cystoscopy for patients undergoing detection or surveillance of bladder cancer.Department of Veterans Affairs BLR&D Merit Review I01 BX004962 to JCL.
      https://www.ncbi.nlm.nih.gov/pubmed/41955017 https://doi.org/10.1172/JCI203094
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    • Rapid and robust diagnosis of bloodstream infections by single-cell analysis.

      Chin, S. M., Epiphaniou, E., Kovtunov, E., Mach, K. E., Liao, J. C., Bobenchik, A. M., Yang, S., Wong, P. K.

      Science advances

      ABSTRACT
      Sepsis, a common consequence of bloodstream infection (BSI), is a leading cause of global mortality and morbidity. Diagnosis is challenging and slow due to extremely low pathogen loads and reliance on time-consuming blood culture, necessitating empiric therapy that worsens outcomes and antimicrobial resistance. Here, we introduce sedimentation-assisted tandem rocking and enrichment for analysis and monitoring (STREAM), a platform that efficiently isolates and enriches pathogens near their natural doubling times directly from whole blood. By integrating STREAM with single-cell analysis, we achieved rapid and robust diagnosis of BSI from whole-blood samples. Using molecularly barcoded sequential fluorescence in situ hybridization, pathogen identification in 104 positive blood culture samples showed 96.15% concordance with clinical laboratory results. Gel-based single-cell antimicrobial susceptibility testing yields 97.96% essential agreement and 93.9% categorical agreement across 219 drug-dose combinations. The platform enabled comprehensive BSI diagnosis directly from whole blood, achieving a detection limit as low as 0.1 to 1 CFU/ml and delivering full diagnostic results within 6.75 to 17 hours.
      https://www.ncbi.nlm.nih.gov/pubmed/42647646 https://doi.org/10.1126/sciadv.aeh3580
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    • The vanguard study: Assessing feasibility of conducting randomized trials of novel multi-cancer detection tests.

      Guthrie, K. A., Anderson, G. L., Bell-Brown, A., Cook, L. S., Doescher, M. P., Etzioni, R. B., Feng, Z., Henderson, L. M., Herman, J., Honda, S. A., James, A. S., Kaltman, R., Kooperberg, C., Krist, A. H., Lee, C. B., Lee, J. K., LeeVan, E., Liao, J. C., Minasian, L. M., Moore, K. N., Nana-Sinkam, P., Natale, K. E., Neslund-Dudas, C., Ngo-Metzger, Q., Ong, M. J., Oshiro, C. E., Park, Y., Patriotis, C., Pinsky, P., Prorok, P. C., Reuland, D. S., Ritzwoller, D. P., Rubinstein, W. S., Sakoda, L. C., Sayles, J., Schenk, J. M., Shofer, S., Simoff, M., Toriola, A. T., White, L., Wion, E. S., Wood, M., Ramsey, S. D.

      Contemporary clinical trials

      ABSTRACT
      A new generation of cancer screening tests, Multi-Cancer Detection (MCD) tests, aims to advance cancer early detection. MCD tests combine blood-based assays of biological components and bioinformatics algorithms to simultaneously detect multiple types of cancer. If shown to improve health outcomes, these new technologies would be simple to implement, improving accessibility and uptake. Importantly, MCD tests offer early detection opportunities for cancers with no established screening modalities. Despite their promise, evidence supporting public health benefits of MCD tests for early detection benefits is lacking. No consensus yet exists for determining the level of screening performance or the effects on cancer outcomes needed to recommend adoption. Further, the potential harms of screening (e.g., unnecessary procedures, increased anxiety and cancer worry) are often overlooked. To address the need for objective evaluation of novel screening methods, the National Cancer Institute created the Cancer Screening Research Network (CSRN). The CSRN mission is to implement rigorous clinical trials to evaluate a broad range of technologies and approaches for cancer screening. The CSRN Vanguard Study is the first US randomized trial of MCDs. The study will develop and determine the feasibility of critical protocol components of a subsequent full-scale trial to evaluate MCD tests. The study will enroll 18,000-24,000 participants aged 45-75 years without known cancer and randomize them into one of 3 arms: two separate intervention arms each evaluating one MCD test, or a control arm. Information gained will inform full-scale trial design and implementation features. ClinicalTrials.gov Identifier: NCT06995898; https://clinicaltrials.gov/study/NCT06995898; registered May 29, 2025.
      https://www.ncbi.nlm.nih.gov/pubmed/42571808 https://doi.org/10.1016/j.cct.2026.108418
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    • Future Directions: Artificial Intelligence and Digital Tools in Bladder Cancer Care.

      Jiang, T., Zhao, C. C., Liao, J. C.

      The Urologic clinics of North America

      ABSTRACT
      This comprehensive review examines using artificial intelligence (AI) across the diagnostic, therapeutic, and prognostic landscape of bladder cancer. It highlights AI's capacity to enhance tumor detection, histopathologic interpretation, surgical precision, and personalized treatment planning through machine learning and computer vision. Applications such as AI-assisted cystoscopy, cytology, transurethral resection of bladder tumor optimization, and prognostic modeling demonstrate significant potential to reduce variability and improve clinical decision-making. While early evidence is promising, widespread implementation requires rigorous validation, multicenter collaboration, and regulatory standardization. Ultimately, AI represents a paradigm shift toward data-driven, precision-based bladder cancer management which can potentially transform patient outcomes globally.
      https://www.ncbi.nlm.nih.gov/pubmed/42362307 https://doi.org/10.1016/j.ucl.2026.03.013
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    • Histopathologic and spatial transcriptomic analyses of en bloc resections of de novo and recurrent bladder tumors.

      Peterson, D., Pacheco, R., Lu, G., Liao, J. C., Dong, F., Chan, E., Prado, K.

      https://doi.org/10.1200/JCO.2026.44.7_suppl.864
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    • Limitations of ultrasound compared with computed tomography for kidney stone surveillance.

      Sun, R., Sommer, E., Ganesan, C., Pao, A. C., Liao, J., Leppert, J., Chang, H., Conti, S., Chang, T.

      Canadian Urological Association journal = Journal de l'Association des urologues du Canada

      ABSTRACT
      Renal ultrasound (US) offers less radiation exposure than computed tomography (CT) for kidney stone surveillance but has lower sensitivity and specificity for nephrolithiasis diagnosis. Additionally, US may overestimate stone size, leading to unnecessary surgical interventions. Evidence on US performance for kidney stone surveillance is variable, making its clinical utility unclear. We aimed to assess US accuracy against CT and identify factors influencing US performance.We performed a retrospective review of patients with known nephrolithiasis seen in urology clinic at Stanford who underwent both renal US and CT within 90 days for surveillance from January to December 2022. Patients with spontaneous stone passage or interventions were excluded. Stone characteristics were recorded, and statistical analysis compared the diagnostic accuracy of US and CT.A total of 107 patients and 128 stones were included, with a mean time difference of 25.7 days between US and CT. US sensitivity was 77%, with a positive predictive value (PPV) of 75% for stone detection. The PPV was only 59% for stones >4 mm by CT. Mean stone size was 8.7 mm on US vs. 5.5 mm on CT (p=0.02), with more pronounced overestimation in smaller stones and higher body mass index (BMI) (p<0.05). No significant differences in US performance were found by stone location, laterality, or time between scans. Differences in stone detection (p=0.01) and size (p=0.03) were associated with the individual performing the ultrasound.US performance is limited compared to CT and is influenced by stone size, BMI, and sonographer. Overestimation by US may lead to unnecessary interventions in up to 40% of patients with stones >4 mm.
      https://www.ncbi.nlm.nih.gov/pubmed/40116666 https://doi.org/10.5489/cuaj.9043
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    • Disrupting Biofilms on Human Kidney Stones-A Path Toward Reducing Infectious Complications During Stone Surgery.

      Massana Roquero, D., Holton, G. H., Ge, T. J., Kornberg, Z., Mach, K. E., Rodriguez, G., La, V., Lau, H., Sun, R., Chang, T. C., Conti, S., Liao, J. C.

      Advanced healthcare materials

      ABSTRACT
      Kidney stones are a common disorder associated with significant morbidity and often requires surgical intervention. Pathogenic bacteria are found in almost 40% of stones, where they form biofilms that are protected from systemic antibiotic treatments. Stone surgeries disperse biofilms resulting in up to 30% of patients developing postoperative urinary tract infections and 15% developing sepsis. This work is based on the hypothesis that chitosan, an antimicrobial polymer, can eradicate bacterial biofilms present in the stone and potentially serve as an adjunct to irrigation during stone surgery. First, fresh patient-derived kidney stone fragments (n = 56) are collected from stone surgeries. A total of 32% of stones are colonized, predominantly with Enterococcus faecalis, Escherichia coli, and Proteus mirabilis. A short, clinically relevant, chitosan treatment reduces the bacterial burden on colonized stones by over 90% in all specimens tested, regardless of stone composition and bacterial strain. To assess this approach toxicity, ex vivo human ureters and in vivo porcine bladders are exposed to topical chitosan irrigation. No toxic or pathological abnormalities other than urothelial exfoliation are noted. In conclusion, chitosan effectively disrupts kidney stone-associated bacterial biofilms with minimal urothelial toxicity and may provide an effective and safe approach to reducing postoperative complications.
      https://www.ncbi.nlm.nih.gov/pubmed/40012448 https://doi.org/10.1002/adhm.202403470
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    • Upstaging and risk migration with blue light cystoscopy for non-muscle-invasive bladder cancer: Results from a prospective multi-center registry.

      Ghoreifi, A., Konety, B. R., Pohar, K. S., Holzbeierlein, J., Taylor III, J., Kates, M., Willard, B., Taylor, J. M., Liao, J. C., Kaimakliotis, H. Z., Porten, S. P., Steinberg, G. D., Tyson, M., Lotan, Y., Daneshmand, S.

      https://doi.org/10.1200/JCO.2025.43.5_suppl.686
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    • Microbial Product Cocktails for Personalized Cancer Immunotherapy.

      Yan, Y., Yang, S., Mach, K. E., Chen, G., DeGraff, D. J., Liao, J. C., Wong, P. K.

      Nature communications

      ABSTRACT
      The human immune system has evolved to interact with various commensal and pathogenic bacteria in intricate and nuanced ways. While the diversity of bacteria presents fundamental challenges in understanding immune-microbe interactions, it also offers ample opportunities for developing effective immunomodulatory therapies. Here we show microbial product cocktails as an alternative class of immunotherapy for cancer. Using freshly resected tumors from bladder cancer patients, an immuno-comparative analysis of recruitment and enhancement assay, and an artificial intelligence-guided optimization workflow, we establish a personalized platform for identifying potent microbial product cocktails that promote recruitment, infiltration, and activation of immune cells for cancer treatment. In an orthotopic mouse bladder cancer model, microbial product cocktails demonstrate immunoenhancement and improve survival rates compared with standard bacillus Calmette-Guérin immunotherapy.
      https://www.ncbi.nlm.nih.gov/pubmed/41331244 https://doi.org/10.1038/s41467-025-66238-1
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    • Precision single cell analysis to characterize host dependent antimicrobial response heterogeneity in physiological medium.

      Abe, R., Lee, J. H., Chin, S. M., Ram-Mohan, N., Tjandra, K. C., Bobenchik, A. M., Mach, K. E., Liao, J. C., Wong, P. K., Yang, S.

      Lab on a chip

      ABSTRACT
      Antimicrobial stewardship plays an essential role in combating the global health threat posed by multidrug-resistant pathogens. Phenotypic antimicrobial susceptibility testing (AST) is the gold standard for analyzing bacterial responses to antimicrobials. However, current AST techniques, which rely on end-point bulk measurements of bacterial growth under antimicrobial treatment in a broth solution, have limitations in resembling the physiological working environment and resolving heterogeneity in response kinetics within the population. In this study, we investigate the responses of uropathogenic bacteria under antimicrobial treatment in individual urine. Our results demonstrate substantial heterogeneity in time-kill kinetics in response to antimicrobials in a host-dependent manner. We also establish a microfluidic gel encapsulation platform for single cell imaging to rapidly resolve heterogeneous subpopulations in response to antimicrobials. The platform captures both bacterial growth and killing within the gel and enables medium exchange to assess the ability of surviving cells to resume growth after antimicrobial removal. Our study lays the foundation for a new generation of precision single cell analysis for personalizing antimicrobial treatment.
      https://www.ncbi.nlm.nih.gov/pubmed/39898695 https://doi.org/10.1039/d4lc00765d
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    • An Organoid Biosensing Platform for Personalized Prognosis of Bladder Cancer.

      Yan, Y., Ahmed, M., Mach, K. E., Liao, J. C., Wong, P. K.

      ACS nano

      ABSTRACT
      Bladder cancer is a common malignancy with high rates of recurrence and progression. Chemotherapy is employed at various stages of treatment to improve patient outcomes and survival; however, it is sometimes ineffective and often associated with significant side effects. Moreover, growing evidence suggests that chemotherapy may paradoxically enhance tumor invasiveness and metastatic potential in certain cancer types. These concerns underscore the need to better understand the impact of chemotherapy and to develop personalized prognostic strategies for managing bladder cancer. Here, we present an organoid biosensing platform designed to evaluate chemotherapy-induced effects in a patient-specific manner. Freshly isolated tumor organoids and self-assembled microtumors derived from transurethral resection of bladder tumors are cultured within a tumor-on-gel biomimetic microenvironment. By integrating live single cell nanobiosensors with 3D time-lapse microscopy, the platform enables real-time detection of molecular biomarkers and rapid assessment of tumor invasiveness. We demonstrate the platform's capability to evaluate cisplatin-induced invasion in bladder cancer organoids at the individual patient level. These findings highlight the potential of this organoid biosensing system to investigate patient-specific risks of chemotherapy-induced invasion and to guide more personalized approaches to bladder cancer treatment.
      https://www.ncbi.nlm.nih.gov/pubmed/41166099 https://doi.org/10.1021/acsnano.5c09883
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    • AI-Driven Defecation Analysis by Smart Healthcare Toilet: Exploring Biometric Patterns and Eu-Tenesmus.

      Song, Z., Kwon, T., Lee, J., Won, D. D., Lee, B. J., Choi, H. S., Liao, J. C., Park, W. G., Sonu, I., Rogalla, S., Rosen, M. J., Hu, D. L., Ziyang, J. K., Wong, S. H., Jun, B. H., Kim, S., Park, S. M.

      Advanced science (Weinheim, Baden-Wurttemberg, Germany)

      ABSTRACT
      Defecation, a fundamental physiological process, remains underexplored despite its importance in human health. To address this gap, a smart toilet system is developed that enables real-time monitoring of defecation behaviors. Analyzing 45 defecation events from 11 participants, key defecation parameters are identified, including stool dropping duration, stool thickness, and eu-tenesmus interval. Stool dropping duration follows a log-normal distribution, with longer durations (>5 s) linked to lower Bristol Stool Form Scale (BSFS) scores, suggesting constipation (p = 0.008 for BSFS1/2/3 vs BSFS5/6/7). Stool thickness decreases with increasing BSFS scores (p = 5 × 10⁻⁶ for BSFS1/2/3 vs BSFS5/6/7), validating its role as an objective marker for bowel function. Eu-tenesmus is introduced, defined as the interval between the last stool drop and cleansing, averaging 74.8 s. It shows significant gender differences (p = 0.014) but no correlation with stool consistency, suggesting its potential as an independent biomarker for gut health. Defecation behaviors between humans and animals is also compared in detail. Longitudinal monitoring demonstrates the potential for personalized health tracking and dietary recommendations. Furthermore, the feasibility of biometric identification is established using 11 defecation-related parameters, including stool properties and cleansing behavior. These features enable high participant differentiation, supporting non-invasive identity verification.
      https://www.ncbi.nlm.nih.gov/pubmed/40349171 https://doi.org/10.1002/advs.202503247
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    • Perspective on the use of optics in bladder cancer detection and diagnosis.

      Remmelink, M. J., Peterson, D. J., Nieuwenhuijzen, J. A., van Leeuwen, T. G., Liao, J. C., de Bruin, D. M.

      Journal of biomedical optics

      ABSTRACT
      Bladder cancer (BC) diagnosis, management, and outcomes depend on the accurate detection of tumors via optical technologies. Accordingly, understanding the benefits and limitations of these technologies permits improvements in patient care and identifies areas for future research.We outline the current process of BC detection and diagnosis, explore the current role of optical technologies, and discuss the opportunities and challenges they present in this field.The current diagnostic pathway of BC, the use of optical technologies, and their shortcomings in this process are reviewed. From there, opportunities and challenges of optics in BC detection and diagnosis are discussed.BC management is expensive due to the limitations of white light cystoscopy, the requirement for histopathological confirmation, and the need for long-term surveillance. Alternative non-optical methods lack accuracy, and available optical techniques focus only on cancer detection. Alternatives to histopathology need to provide accurate real-time results to be effective. Optical advancements offer potential benefits; however, challenges include cost-effectiveness, device complexity, required training, and tumor heterogeneity.Optical techniques could accelerate BC diagnosis, reduce costs, and enable alternative treatments. However, overcoming technical and practical challenges is essential for their successful integration.
      https://www.ncbi.nlm.nih.gov/pubmed/40190592 https://doi.org/10.1117/1.JBO.30.4.040601
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    • Electronic Documentation of Intraoperative Observation of Cystoscopic Procedures Using the cMDX Information System.

      Eminaga, O., Lee, T. J., La, V., Breil, B., Xing, L., Liao, J. C.

      JCO clinical cancer informatics

      ABSTRACT
      PURPOSE: Accurate documentation of lesions during transurethral resection of bladder tumors (TURBT) is essential for precise diagnosis, treatment planning, and follow-up care. However, optimizing schematic documentation techniques for bladder lesions has received limited attention.MATERIALS AND METHODS: This prospective observational study used a cMDX-based documentation system that facilitates graphical representation, a lesion-specific questionnaire, and heatmap analysis with a posterization effect. We designed a graphical scheme for bladder covering bladder landmarks to visualize anatomic features and to document the lesion location. The lesion-specific questionnaire was integrated for comprehensive lesion characterization. Finally, spatial analyses were applied to investigate the anatomic distribution patterns of bladder lesions.RESULTS: A total of 97 TURBT cases conducted between 2021 and 2023 were included, identifying 176 lesions. The lesions were distributed in different bladder areas with varying frequencies. The distribution pattern, sorted by frequency, was observed in the following areas: posterior, trigone, lateral right and anterior, and lateral left and dome. Suspicious levels were assigned to the lesions, mostly categorized either as indeterminate or moderate. Lesion size analysis revealed that most lesions fell between 5 and 29 mm.CONCLUSION: The study highlights the potential of schematic documentation techniques for informed decision making, quality assessment, primary research, and secondary data utilization of intraoperative data in the context of TURBT. Integrating cMDX and heatmap analysis provides valuable insights into lesion distribution and characteristics.
      https://www.ncbi.nlm.nih.gov/pubmed/38484216 https://doi.org/10.1200/CCI.23.00114
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