Optical biopsy of bladder cancer using confocal laser endomicroscopy

Prospective Analysis of Confocal Laser Endomicroscopy for Assessment of the Resection Bed for Bladder Tumor

Background and objective

Urothelial bladder cancer (UCB) care requires frequent follow-up cystoscopy and surgery. Confocal laser endomicroscopy (CLE), a probe-based optical technique for real-time microscopic evaluation, has shown promising accuracy for grading of UCB. We investigated the diagnostic accuracy of CLE-based assessment of the surgical radicality of the bladder resection bed (RB).

Methods

We prospectively included 40 participants scheduled for transurethral resection of bladder tumors (TURBT) in two academic hospitals. Exclusion criteria were flat lesions, fluorescein allergy, and pregnancy. We performed CLE of the RB during TURBT. Histopathology of an RB biopsy was the reference test. Results at first cystoscopy 3 mo after TURBT are reported. A panel of two blinded observers evaluated the CLE images. The diagnostic accuracy of CLE for detection of detrusor muscle (DM) and residual tumor (rT) was calculated using 2 × 2 tables.

Key findings and limitations

Histopathology for 22 CLE-matched RB biopsies revealed rT in four cases (18%) and DM in 13 (59%). The quality of CLE imaging was low in four (18%), moderate in 16 (73%), and good in two (9%) cases. CLE was able to correctly predict rT in two of the four cases (50%) identified on histopathology. The sensitivity, specificity, positive predictive value, and negative predictive value were 0.5 (95% confidence interval [CI] 0.07-0.93), 0.83 (95% CI 0.59-0.96), 0.4 (95% CI 0.05-0.85), and 0.88 (95% CI 0.64-0.99) for CLE prediction of rT, and 0.69 (95% CI 0.39-0.91), 0.33 (95% CI 0.07-0.7), 0.6 (95% CI 0.32-0.84), and 0.43 (95% CI 0.1-0.82) for prediction of DM, respectively. Five patients (23%) had rT at 3-mo follow-up; CLE had predicted rT in three, and histopathology had revealed rT in two cases at TURBT.

Conclusions and clinical implications

CLE does not appear to be a reliable tool for detecting rT or DM in the RB after TURBT.

Patient summary

We investigated a special imaging technique called confocal laser endomicroscopy (CLE) for checking the bladder after surgery for bladder cancer in a group of 40 patients. CLE results were compared to traditional biopsy results and the patients were checked after 3 months. CLE was not very reliable in detecting any remaining cancer (only 50% accurate) or important muscle tissue in the surgical area, and the quality of the images varied. While CLE shows some promise, it is not currently a dependable method for evaluating the bladder after bladder cancer surgery.

A novel artificial intelligence segmentation model for early diagnosis of bladder tumors

OBJECTIVE

Despite cystoscopy plays an important role in bladder tumors diagnosis, it often falls short in flat cancerous tissue and minuscule satellite lesions. It can easily lead to a missed diagnosis by the urologist, which can lead to a swift tumor regrowth following transurethral resection of the bladder tumor (TURBT). Therefore, we developed a deep learning-based intelligent diagnosis system for early bladder cancer to improve the identification rate of early bladder tumors.

METHODS

Video data from 273 bladder cancer patients who underwent TURBT at Zhongnan Hospital were collected. The dataset was carefully annotated by urologists to clearly define tumor boundaries. Subsequently, we developed a new bladder tumor segmentation network (BTS-Net) based on transformer to accurately diagnose early-stage bladder cancer lesions.

RESULTS

Our experiments demonstrate that the BTS-Net we developed has outperformed other method on the external B validation dataset, achieving a MPrecision of 91.39%, a MRecall of 95.71%, a MIoU of 88.18% and an F1-score of 93.18%. The BTS-Net showed high accuracy with real-time processing speed at 23 fps.

CONCLUSION

Missed detection of satellite lesions in early bladder tumors often leads to tumor recurrence. Our BTS-Net is capable of segmenting all potential satellite lesions in surgical videos, without the need for complex professional equipment. This AI-assisted diagnosis system has the potential to improve surgical outcomes by ensuring comprehensive treatment of all tumor-related areas during TURBT.

Advanced optical imaging techniques for bladder cancer detection and diagnosis: a systematic review

OBJECTIVES

To systematically assess the current available literature concerning advanced optical imaging methods for the detection and diagnosis of bladder cancer (BCa), focusing particularly on the sensitivity and specificity of these techniques.

METHODS

First a scoping search was performed to identify all available optical techniques for BCa detection and diagnosis. The optical imaging techniques used for detecting BCa are: the Storz professional image enhancement system (IMAGE1 S), narrow-band imaging (NBI), photoacoustic imaging (PAI), autofluorescence imaging (AFI), photodynamic diagnosis (PDD), and scanning fibre endoscopy (SFE). The staging and grading techniques for BCa are: optical coherence tomography (OCT), confocal laser endomicroscopy (CLE), Raman spectroscopy, endocytoscopy, and non-linear optical microscopy (NLO). Then a systematic literature search was conducted using MEDLINE, EMBASE and Web of Science from inception to 21 November 2023. Articles were screened and selected by two independent reviewers. Inclusion criteria were: reporting on both the sensitivity and specificity of a particular technique and comparison to histopathology, and in the case of a detection technique comparison to white light cystoscopy (WLC).

RESULTS

Out of 6707 articles, 189 underwent full-text review, resulting in 52 inclusions. No articles met criteria for IMAGE1 S, PAI, SFE, Raman spectroscopy, and endocytoscopy. All detection techniques showed higher sensitivity than WLC, with NBI leading (87.8-100%). Overall, detection technique specificity was comparable to WLC, with PDD being most specific (23.3-100%). CLE and OCT varied in sensitivity and specificity, with OCT showing higher specificity for BCa diagnosis, notably for carcinoma in situ (97-99%) compared to CLE (62.5-81.3%). NLO demonstrated high sensitivity and specificity (90-97% and 77-100%, respectively) based on limited data from two small ex vivo studies.

CONCLUSIONS

Optical techniques with the most potential are PDD for detecting and OCT for staging and grading BCa. Further research is crucial to validate their integration into routine practice and explore the value of other imaging techniques.

Grading urothelial carcinoma with probe-based confocal laser endomicroscopy during flexible cystoscopy

PURPOSE

Urothelial bladder cancer (UCB) care requires frequent follow-up cystoscopy and surgery. Confocal laser endomicroscopy (CLE) is a probe-based optical technique that can provide real-time microscopic evaluation with the potential for outpatient grading of UCB. This study aims to investigate the diagnostic accuracy and interobserver variability for the grading of UCB with CLE during flexible cystoscopy (fCLE).

METHODS

Participants scheduled for transurethral resection of papillary bladder tumors were prospectively included for intra-operative fCLE. Exclusion criteria were flat lesions, fluorescein allergy or pregnancy. Two independent observers evaluated fCLE, classifying tumors as low- or high-grade urothelial carcinoma (LGUC/HGUC) or benign. Interobserver agreement was calculated with Cohens kappa (κ) and diagnostic accuracy with 2 × 2 tables. Histopathology was the reference test.

RESULTS

Histopathology of 34 lesions revealed 14 HGUC, 14 LGUC and 6 benign tumors. Diagnostic yield for fCLE was 80-85% with a κ of 0.75. Respectively, sensitivity, specificity, NPV and PPV were: for benign tumors 0-20%, 96-100%, unmeasureable-50% and 87%, for LGUC 57-64%, 41-58%, 44-53% and 54-69% and for HGUC 38-57%, 56-68%, 38-57% and 56-68%, with an interobserver agreement of κ 0.61.

CONCLUSION

fCLE is currently insufficient to grade UCB.

Fluorescein-stained confocal laser endomicroscopy versus conventional frozen section for intraoperative histopathological assessment of intracranial tumors

BACKGROUND

The aim of this clinical trial was to compare Fluorescein-stained intraoperative confocal laser endomicroscopy (CLE) of intracranial lesions and evaluation by a neuropathologist with routine intraoperative frozen section (FS) assessment by neuropathology.

METHODS

In this phase II noninferiority, prospective, multicenter, nonrandomized, off-label clinical trial (EudraCT: 2019-004512-58), patients above the age of 18 years with any intracranial lesion scheduled for elective resection were included. The diagnostic accuracies of both CLE and FS referenced with the final histopathological diagnosis were statistically compared in a noninferiority analysis, representing the primary endpoint. Secondary endpoints included the safety of the technique and time expedited for CLE and FS.

RESULTS

A total of 210 patients were included by 3 participating sites between November 2020 and June 2022. Most common entities were high-grade gliomas (37.9%), metastases (24.1%), and meningiomas (22.7%). A total of 6 serious adverse events in 4 (2%) patients were recorded. For the primary endpoint, the diagnostic accuracy for CLE was inferior with 0.87 versus 0.91 for FS, resulting in a difference of 0.04 (95% confidence interval -0.10; 0.02; P = .367). The median time expedited until intraoperative diagnosis was 3 minutes for CLE and 27 minutes for FS, with a mean difference of 27.5 minutes (standard deviation 14.5; P < .001).

CONCLUSIONS

CLE allowed for a safe and time-effective intraoperative histological diagnosis with a diagnostic accuracy of 87% across all intracranial entities included. The technique achieved histological assessments in real time with a 10-fold reduction of processing time compared to FS, which may invariably impact surgical strategy on the fly.

Fluorescence Confocal Microscopy in Urological Malignancies: Current Applications and Future Perspectives

Fluorescence confocal microscopy (FCM) represents a novel diagnostic technique able to provide real-time histological images from non-fixed specimens. As a consequence of its recent developments, FCM is gaining growing popularity in urological practice. Nevertheless, evidence is still sparse, and, at the moment, its applications are heterogeneous. We performed a narrative review of the current literature on this topic. Papers were selected from the Pubmed, Embase, and Medline archives. We focused on FCM applications in prostate cancer (PCa), urothelial carcinoma (UC), and renal cell carcinoma (RCC). Articles investigating both office and intraoperative settings were included. The review of the literature showed that FCM displays promising accuracy as compared to conventional histopathology. These results represent significant steps along the path of FCM's formal validation as an innovative ready-to-use diagnostic support in urological practice. Instant access to a reliable histological evaluation may indeed significantly influence physicians' decision-making process. In this regard, FCM addresses this still unmet clinical need and introduces intriguing perspectives into future diagnostic pathways. Further studies are required to thoroughly assess the whole potential of this technique.

Disease Management of Clinical Complete Responders to Neoadjuvant Chemotherapy of Muscle-Invasive Bladder Cancer: A Review of Literature

Muscle-invasive bladder cancer (MIBC) is an aggressive disease requiring active management. Neoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) is considered the standard treatment paradigm for MIBC patients, which could result in significant perioperative mortality and morbidity, as well as the significant alteration of the quality of life (QOL). Notably, multimodal bladder-preserving treatment strategies have been recommended for highly selected patients. Pathologic complete response (pCR) after NAC is a powerful prognostic indicator of survival for patients with MIBC. Clinical complete response (cCR) is then introduced as a complementary endpoint for pCR to assess disease status preoperatively. Bladder preservation strategy for patients who achieve cCR following NAC is emerging as a new treatment concept. However, the efficiency of the conservative strategy remains controversial. In this state-of-the-art review, we discuss the advantages and limitations of cCR and the feasibility and safety of bladder preservation strategy in highly selected MIBC patients who achieve cCR following NAC. We conclude that a conservative strategy can be considered a reasonable alternative to RC in carefully selected cCR MIBC patients, leading to acceptable oncological outcomes.

The development and clinical application of microscopic endoscopy for in vivo optical biopsies: Endocytoscopy and confocal laser endomicroscopy

Endoscopies are crucial for detecting and diagnosing diseases in gastroenterology, pulmonology, urology, and other fields. To accurately diagnose diseases, sample biopsies are indispensable and are currently considered the gold standard. However, random 4-quadrant biopsies have sampling errors and time delays. To provide intraoperative real-time microscopic images of suspicious lesions, microscopic endoscopy for in vivo optical biopsy has been developed, including endocytoscopy and confocal laser endomicroscopy. This article reviews recent advances in technology and clinical applications, as well as their shortcomings and future directions.

Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors

Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of flat and small lesions. Here, we aim to demonstrate a BL image-based artificial intelligence (AI) diagnostic platform using 216 BL images, that were acquired in four different urological departments and pathologically identified with respect to cancer malignancy, invasiveness, and grading. Thereafter, four pre-trained convolution neural networks were utilized to predict image malignancy, invasiveness, and grading. The results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84%, while the mean sensitivity and mean specificity of tumor invasiveness are 88% and 96.56%, respectively. This small multicenter clinical study clearly shows the potential of AI based classification of BL images allowing for better treatment decisions and potentially higher detection rates.