Publications

Abstract

Diagnostic cystoscopy in combination with transurethral resection of the bladder tumour are the standard for the diagnosis, surgical treatment and surveillance of bladder cancer. The ability to inspect the bladder in its current form stems from a long chain of advances in imaging science and endoscopy. Despite these advances, bladder cancer recurrence and progression rates remain high after endoscopic resection. This stagnation is a result of the heterogeneity of cancer biology as well as limitations in surgical techniques and tools, as incomplete resection and provider-specific differences affect cancer persistence and early recurrence. An unmet clinical need remains for solutions that can improve tumour delineation and resection. Translational advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to overcoming the progress plateau.

View details for DOI 10.1038/s41585-024-00904-9

View details for PubMedID 38982304

View details for PubMedCentralID 6889816

Abstract

Detection of bladder tumors under white light cystoscopy (WLC) is challenging yet impactful on treatment outcomes. Artificial intelligence (AI) holds the potential to improve tumor detection; however, its application in the real-time setting remains unexplored. AI has been applied to previously recorded images for post hoc analysis. In this study, we evaluate the feasibility of real-time AI integration during clinic cystoscopy and transurethral resection of bladder tumor (TURBT) on live, streaming video.Patients undergoing clinic flexible cystoscopy and TURBT were prospectively enrolled. A real-time alert device system (real-time CystoNet) was developed and integrated with standard cystoscopy towers. Streaming videos were processed in real time to display alert boxes in sync with live cystoscopy. The per-frame diagnostic accuracy was measured.Real-time CystoNet was successfully integrated in the operating room during TURBT and clinic cystoscopy in 50 consecutive patients. There were 55 procedures that met the inclusion criteria for analysis including 21 clinic cystoscopies and 34 TURBTs. For clinic cystoscopy, real-time CystoNet achieved per-frame tumor specificity of 98.8% with a median error rate of 3.6% (range: 0 - 47%) frames per cystoscopy. For TURBT, the per-frame tumor sensitivity was 52.9% and the per-frame tumor specificity was 95.4% with an error rate of 16.7% for cases with pathologically confirmed bladder cancers.The current pilot study demonstrates the feasibility of using a real-time AI system (real-time CystoNet) during cystoscopy and TURBT to generate active feedback to the surgeon. Further optimization of CystoNet for real-time cystoscopy dynamics may allow for clinically useful AI-augmented cystoscopy.

View details for DOI 10.1089/end.2023.0056

View details for PubMedID 37432899

Abstract

Only 60-75% of conventional kidney stone surgeries achieve complete stone-free status. Up to 30% of patients with residual fragments <2 mm in size experience subsequent stone-related complications. Here we demonstrate a stone retrieval technology in which fragments are rendered magnetizable with a magnetic hydrogel so that they can be easily retrieved with a simple magnetic tool. The magnetic hydrogel facilitates robust in vitro capture of stone fragments of clinically relevant sizes and compositions. The hydrogel components exhibit no cytotoxicity in cell culture and only superficial effects on ex vivo human urothelium and in vivo mouse bladders. Furthermore, the hydrogel demonstrates antimicrobial activity against common uropathogens on par with that of common antibiotics. By enabling the efficient retrieval of kidney stone fragments, our method can lead to improved stone-free rates and patient outcomes.

View details for DOI 10.1038/s41467-023-38936-1

View details for PubMedID 37349287

View details for PubMedCentralID 5853829

View details for DOI 10.1038/s43586-022-00190-y

View details for Web of Science ID 000925774700001

Abstract

OBJECTIVES: The risk of bladder cancer (BCa) diagnosis and recurrence necessitates cystoscopy. Improved risk stratification may inform personalized triage and surveillance strategies. We aim to develop a urinary mRNA biomarker panel for risk stratification in patients undergoing BCa screening and surveillance.METHODS AND MATERIALS: Urine samples were collected from patients undergoing cystoscopy for BCa screening or surveillance. In patients who underwent transurethral resection of bladder tumor, urine samples were categorized based on tumor histopathology, size, and focality. Subjects with intermediate and high-risk BCa based on American Urological Association (AUA) guideline for non-muscle invasive bladder cancer were classified as "increased-risk"; those with no cancer and AUA low-risk BCa were classified as "low-risk". Urine was evaluated for ROBO1, WNT5A, CDC42BPB, ABL1, CRH, IGF2, ANXA10, and UPK1B expression. A diagnostic model to detect "increased-risk" BCa was created using forward logistic regression analysis of cycle threshold values. Model validation was performed with ten-fold cross-validation. Sensitivity and specificity for detection of "increased-risk" BCa was determined and net benefit analysis performed.RESULTS: Urine samples (n = 257) were collected from 177 patients (95 screening, 76 surveillance, 6 both). There were 65 diagnoses of BCa (12 low, 22 intermediate, 31 high risk). ROBO1, CRH, and IGF2 expression correlated with "increased-risk" disease yielding sensitivity of 92.5% (95% CI, 84.9%-98.1%) and specificity of 73.5% (95% CI, 67.7-79.9%). The overall calculated standardized net benefit of the model was 0.81 (95%CI, 0.71-0.90).CONCLUSIONS: A 3-marker urinary mRNA panel allows for non-invasive identification of "increased-risk" BCa and with further validation may prove to be a tool to reduce the need for cystoscopies in low-risk patients.

View details for DOI 10.1016/j.urolonc.2021.02.011

View details for PubMedID 33766467

View details for DOI 10.1002/advs.202003419

View details for Web of Science ID 000613612200001

Abstract

PURPOSE: Near-infrared photoimmunotherapy (NIR-PIT) is a localized molecular cancer therapy combining a photosensitizer-conjugated monoclonal antibody and light energy. CD47 is an innate immune checkpoint widely expressed on bladder cancer cells but absent from luminal normal urothelium. Targeting CD47 for NIR-PIT has the potential to selectively induce cancer cell death and minimize damage to normal urothelium.EXPERIMENTAL DESIGN: The cytotoxic effect of NIR-PIT with anti-CD47-IR700 was investigated in human bladder cancer cell lines and primary human bladder cancer cells derived from fresh surgical samples. Phagocytosis assays were performed to evaluate macrophage activity after NIR-PIT. Anti-CD47-IR700 was administered to murine xenograft tumor models of human bladder cancer for in vivo molecular imaging and NIR-PIT.RESULTS: Cytotoxicity in cell lines and primary bladder cancer cells significantly increased in a light-dose dependent manner with CD47-targeted NIR-PIT. Phagocytosis of cancer cells significantly increased with NIR-PIT compared to antibody alone (p=0.0002). In vivo fluorescence intensity of anti-CD47-IR700 in tumors reached a peak 24-hour post injection and was detectable for at least 14 days. After a single round of CD47-targeted NIR-PIT, treated animals showed significantly slower tumor growth compared to controls (p<0.0001). Repeated CD47-targeted NIR-PIT treatment further slowed tumor growth (p=0.0104) and improved survival compared to controls.CONCLUSION: CD47-targeted NIR-PIT increased direct cancer cell death and phagocytosis resulting in inhibited tumor growth and improved survival in a murine xenograft model of human bladder cancer.

View details for PubMedID 30890547

Abstract

Adequate tumor detection is critical in complete transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence, but up to 20% of bladder tumors are missed by standard white light cystoscopy. Deep learning augmented cystoscopy may improve tumor localization, intraoperative navigation, and surgical resection of bladder cancer. We aimed to develop a deep learning algorithm for augmented cystoscopic detection of bladder cancer. Patients undergoing cystoscopy/TURBT were recruited and white light videos were recorded. Video frames containing histologically confirmed papillary urothelial carcinoma were selected and manually annotated. We constructed CystoNet, an image analysis platform based on convolutional neural networks, for automated bladder tumor detection using a development dataset of 95 patients for algorithm training and five patients for testing. Diagnostic performance of CystoNet was validated prospectively in an additional 54 patients. In the validation dataset, per-frame sensitivity and specificity were 90.9% (95% confidence interval [CI], 90.3-91.6%) and 98.6% (95% CI, 98.5-98.8%), respectively. Per-tumor sensitivity was 90.9% (95% CI, 90.3-91.6%). CystoNet detected 39 of 41 papillary and three of three flat bladder cancers. With high sensitivity and specificity, CystoNet may improve the diagnostic yield of cystoscopy and efficacy of TURBT. PATIENT SUMMARY: Conventional cystoscopy has recognized shortcomings in bladder cancer detection, with implications for recurrence. Cystoscopy augmented with artificial intelligence may improve cancer detection and resection.

View details for DOI 10.1016/j.eururo.2019.08.032

View details for PubMedID 31537407

Abstract

Current regimens for the detection and surveillance of bladder cancer (BLCA) are invasive and have suboptimal sensitivity. Here, we present a novel high-throughput sequencing (HTS) method for detection of urine tumor DNA (utDNA) called utDNA CAPP-Seq (uCAPP-Seq) and apply it to 67 healthy adults and 118 patients with early-stage BLCA who either had urine collected prior to treatment or during surveillance. Using this targeted sequencing approach, we detected a median of 6 mutations per BLCA patient and observed surprisingly frequent mutations of the PLEKHS1 promoter (46%), suggesting these mutations represent a useful biomarker for detection of BLCA. We detected utDNA pre-treatment in 93% of cases using a tumor mutation-informed approach and in 84% when blinded to tumor mutation status, with 96-100% specificity. In the surveillance setting, we detected utDNA in 91% of patients who ultimately recurred, with utDNA detection preceding clinical progression in 92% of cases. uCAPP-Seq outperformed a commonly used ancillary test (UroVysion, p=0.02) and cytology and cystoscopy combined (p is less than or equal to 0.006), detecting 100% of BLCA cases detected by cytology and 82% that cytology missed. Our results indicate that uCAPP-Seq is a promising approach for early detection and surveillance of BLCA.

View details for PubMedID 30578357

Abstract

Timely and accurate identification and determination of the antimicrobial susceptibility of uropathogens is central to the management of UTIs. Urine dipsticks are fast and amenable to point-of-care testing, but do not have adequate diagnostic accuracy or provide microbiological diagnosis. Urine culture with antimicrobial susceptibility testing takes 2-3 days and requires a clinical laboratory. The common use of empirical antibiotics has contributed to the rise of multidrug-resistant organisms, reducing treatment options and increasing costs. In addition to improved antimicrobial stewardship and the development of new antimicrobials, novel diagnostics are needed for timely microbial identification and determination of antimicrobial susceptibilities. New diagnostic platforms, including nucleic acid tests and mass spectrometry, have been approved for clinical use and have improved the speed and accuracy of pathogen identification from primary cultures. Optimization for direct urine testing would reduce the time to diagnosis, yet these technologies do not provide comprehensive information on antimicrobial susceptibility. Emerging technologies including biosensors, microfluidics, and other integrated platforms could improve UTI diagnosis via direct pathogen detection from urine samples, rapid antimicrobial susceptibility testing, and point-of-care testing. Successful development and implementation of these technologies has the potential to usher in an era of precision medicine to improve patient care and public health.

View details for DOI 10.1038/nrurol.2017.20

View details for PubMedID 28248946

Abstract

A combination of optical imaging technologies with cancer-specific molecular imaging agents is a potentially powerful strategy to improve cancer detection and enable image-guided surgery. Bladder cancer is primarily managed endoscopically by white light cystoscopy with suboptimal diagnostic accuracy. Emerging optical imaging technologies hold great potential for improved diagnostic accuracy but lack imaging agents for molecular specificity. Using fluorescently labeled CD47 antibody (anti-CD47) as molecular imaging agent, we demonstrated consistent identification of bladder cancer with clinical grade fluorescence imaging systems, confocal endomicroscopy, and blue light cystoscopy in fresh surgically removed human bladders. With blue light cystoscopy, the sensitivity and specificity for CD47-targeted imaging were 82.9 and 90.5%, respectively. We detected variants of bladder cancers, which are diagnostic challenges, including carcinoma in situ, residual carcinoma in tumor resection bed, recurrent carcinoma following prior intravesical immunotherapy with Bacillus Calmette-Guérin (BCG), and excluded cancer from benign but suspicious-appearing mucosa. CD47-targeted molecular imaging could improve diagnosis and resection thoroughness for bladder cancer.

View details for DOI 10.1126/scitranslmed.3009457

View details for PubMedID 25355698