Introduction The quality of Transurethral resection of bladder tumor (TURBT) procedures is associated with risk stratification and determines a recurrence risk for patients with bladder cancer. One element of a high-quality TURBT is the presence of detrusor muscle (DM) in specimens. Unfortunately, there are currently no adjunct tools available to urologists to determine intraoperatively whether the resected specimen contains DM.;;Consequently, urologists typically rely on a combination of cystoscopic appearance and resection-based experience judgment to assess if TURBT specimens contain DM. In this proof-of-concept study, we sought to determine if multi-spectral autofluoresence imaging (MS-AFI) can detect DM in near real-time from freshly resected bladder specimens and used intraoperatively for the determination of depth of resection during TURBT. The underlying rationale is that multispectral images can provide a snapshot of the various endogenous tissue fluorophores and, when combined with advanced machine learning, can capture intrinsic biomolecular and morphological differences for DM identification. Methods Fresh tissue specimens were imaged in a suite adjacent to the operating theater following tissue excision during TURBT. To maintain tissue integrity and preserve the routine clinical workflow, no chemicals or other processing steps were applied. After imaging, the tissue specimens underwent formalin fixation and routine tissue processing for histopathologic comparison. The corresponding Hematoxylin and Eosin (H&E) slides of the imaged tissue specimens were digitized and digitally annotated by two pathologists. The original H&E image of the tissue specimen was geometrically transformed to spatially register with the multispectral images acquired for the same specimen, and “tiles” of 1 mm x 1mm were identified from the annotated areas consisting of homogeneous DM and other diverse tissue types and transcribed onto the multispectral images. We performed this preliminary assessment of our multispectral imaging system AURORA™ on 544 tiles (class balance DM+ / DM-; 119/425);from 158 tissue specimens obtained from 91 patients. Results The results from the proof-of-concept study on the tiles using leave-one-patient-out cross-validation provide promising levels of performance in detecting DM with an accuracy of 89.0%, sensitivity of 79.8%, specificity of 91.5%, and a negative predictive value of 94.2%. We used the trained classifier to create a heat map at the full specimen level. The heat maps in Figure 1 display the probability (0%: blue, 80%: red) of DM-presence generated using the classifier trained as reported above. Example representative heatmaps in Figure 1A and 1C show the presence and absence of DM on the tissue specimens respectively and spatially correlate well with the corresponding H&E images of histologically confirmed tissue blocks containing DM (Fig 1B) and devoid of DM (Figure 1D). Conclusions We have developed and tested an autofluorescence-based intraoperative imaging modality featuring multispectral excitation and emission wavelength bands to identify DM in TURBT specimens. This preclinical research study demonstrates that our device is suitable for the detection of DM in freshly excised bladder specimens with good accuracy. The availability of an intraoperative tool to ensure adequate depth of resection could enable surgeons to perform higher quality tumor resections. Additionally, improving quality of initial TURBT procedures could reduce the need for re-TURBTs, and have a positive impact on lowering tumor recurrence. Further clinical studies are needed to validate the clinical application and merit of this device.

The identification of detrusor muscle (DM) in bladder specimens is crucial for the successful TURBT procedure. This study conducted on 103 patients demonstrates the potential of multispectral imaging in accurately identifying DM during surgery.

Context.— Repeated surgery is necessary for 20% to 40% of breast conservation surgeries owing to the unavailability of any adjunctive, accurate, and objective tool in the surgeon’s hand for real-time margin assessment to achieve the desired balance of oncologic and cosmetic outcomes. Objective.— To assess the feasibility of using a multispectral autofluorescence imaging device for discriminating malignant neoplasm from normal breast tissue in pathology as a critical step in the development of a device for intraoperative use, and to demonstrate the device’s utility for use in processing and prioritizing specimens during frozen section and in the pathology grossing room. Design.— We performed a preliminary assessment of our device, called the TumorMAP system, on 172 fresh tissue blocks from 115 patients obtained from lumpectomy specimens at the time of initial gross examination and compared the device results with gold standard pathology evaluation. Results.— The preliminary results demonstrate the potential of our device in detecting breast cancer in fresh tissue samples with a sensitivity of 82%, a specificity of 91%, a positive predictive value of 84%, and a negative predictive value of 89%. Conclusions.— Our results suggest that the TumorMAP system is suitable for the detection of malignant neoplasm in freshly excised breast specimens and has the potential to evaluate resection margins in real time.

Here we present a ratiometric intensity analysis of the Raman spectrum in the HWN region of three breast tissue conditions, namely adipose, fibrous and malignant in a broad sampling of lumpectomy specimens. We demonstrate that these intensity ratios can be used as a “molecular barcode” in breast cancer pathology. Our ratiometric-based approach in HWN is simple, interpretable and exhibits distinct barcodes for different tissue types.