Intra-operative fluorescence-based detection of positive surgical margins during radical prostatectomy: Lessons learned from a pilot ex vivo translational study.

OBJECTIVES: Nerve-sparing techniques during radical prostatectomy have been associated with an increased risk of positive surgical margins. The intra-operative detection of residual prostatic tissue could help mitigate this risk. The objectives of the present study were to assess the feasibility of using an anti-prostate-specific membrane antigen (anti-PSMA) antibody conjugated with a fluorophore to characterize fresh prostate tissue as prostatic or non-prostatic for intra-operative surgical margin detection. METHODS: Fresh prostatic tissue samples were collected from transurethral resections of the prostate (TURP) or prostate biopsies, and either immunolabelled with anti-PSMA antibody conjugated with Alexa Fluor 488 or used as controls. A dedicated, laparoscopy-compliant fluorescence device was developed for real-time fluorescence detection. Confocal microscopy was used as the gold standard for comparison. Spectral unmixing was used to distinguish specific, Alexa Fluor 488 fluorescence from nonspecific autofluorescence. RESULTS: The average peak wavelength of the immuno-labeled TURP samples (n = 4) was 541.7 ± 0.9 nm and of the control samples (n = 4) was 540.8 ± 2.2 nm. Spectral unmixing revealed that these similar measures were explained by significant autofluorescence, linked to electrocautery. Three biopsy samples were then obtained from seven patients and also displayed significant nonspecific fluorescence, raising questions regarding the reproducibility of the fixation of the anti-PSMA antibodies on the samples. Comparing the fluorescence results with final pathology proved challenging due to the small sample size and tissue alterations. CONCLUSIONS: This study showed similar fluorescence of immuno-labeled prostate tissue samples and controls, failing to demonstrate the feasibility of intra-operative margin detection using PSMA immuno-labeling, due to marked tissue autofluorescence. We successfully developed a fluorescence device that could be used intraoperatively in a laparoscopic setting. Use of the infrared range as well as newly available antibodies could prove interesting options for future research.

Robust alignment of prostate histology slices with quantified accuracy.

No current imaging technique is capable of detecting with precision tumors in the prostate. To evaluate each technique, the histology data must be precisely mapped to the imaged data. As the histology slices cannot be assumed to be cut along the same plane as the imaged data were acquired, the registration must be considered as a 3-D problem. This requires the prior alignment of the histology slices. We propose a protocol in which three needles are inserted into the fresh prostate, creating internal fiducial markers visible in the histology slices. Our algorithm then automatically detects and identifies these markers, enabling the automatic rigid alignment of each slice. The accuracy of the algorithm was quantified in simulated images, a beef liver sample in which a validation marker had been created, and ten prostate specimens. The simulated images showed that the algorithm has no associated residual error for a situation where there is no deformation. In the beef liver images, the average accuracy of the alignment was 0.12 ± 0.09 mm at the fiducial markers, and 0.62 ± 0.46 mm at a validation marker positioned approximately 20 mm from the fiducial markers. Concerning the ten prostates, there were 19.2 histology slices on average per specimen. On average, 93.7% of the fiducial markers created were visible in the slices, of which 96.1% were then automatically and correctly detected and identified, enabling an alignment of average accuracy 0.18 ± 0.13 mm at the fiducial markers. As a cancer of volume <0.5 cm(3) is classified as clinically insignificant, the accuracy achieved justified the choice of a rigid registration. An attractive feature of this method is the time required, less than 6 min on average per prostate specimen.

Robustness of estimators of long-range dependence and self-similarity under non-Gaussianity.

Long-range dependence (LRD) and non-Gaussianity are ubiquitous in many natural systems such as ecosystems, biological systems and climate. However, it is not always appreciated that the two phenomena may occur together in natural systems and that self-similarity in a system can be a superposition of both phenomena. These features, which are common in complex systems, impact the attribution of trends and the occurrence and clustering of extremes. The risk assessment of systems with these properties will lead to different outcomes (e.g. return periods) than the more common assumption of independence of extremes. Two paradigmatic models are discussed that can simultaneously account for LRD and non-Gaussianity: autoregressive fractional integrated moving average (ARFIMA) and linear fractional stable motion (LFSM). Statistical properties of estimators for LRD and self-similarity are critically assessed. It is found that the most popular estimators can be biased in the presence of important features of many natural systems like trends and multiplicative noise. Also the LRD and non-Gaussianity of two typical natural time series are discussed.