Skip to content

SPECTRA: Intraoperative Hyperspectral Imaging for Prostate Cancer

SPECTRA: Intraoperative Hyperspectral Imaging for Prostate Cancer

Status
Recruiting
Phases
Unknown
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT07826858
Acronym
SPECTRA
Enrollment
26
Registered
2026-09-17
Start date
2026-08-01
Completion date
2027-12-31
Last updated
2026-09-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Hyperspectral Imaging, Localized Prostate Cancer, Positive Surgical Margin

Keywords

Radical prostatectomy, Localized prostate cancer, Positive Surgical Margins, HyperSpectral Imaging (HSI), Hyperspectral Reflectance Imaging, Resection margins

Brief summary

Radical prostatectomy (RP) is currently one of the gold standard of care in managing localized prostate cancer (Pca). However, despite advances in Robot-Assisted RP (RARP), cancer cells may still be found (up to 42%) at the edge of the removed prostate specimen, commonly referred as Positive Surgical Margins (PSM). PSM are well-known risk factors of adverse oncological outcomes (biochemical recurrence). It is therefore essential to characterize intraoperatively the tumor extent. In RARP, the leading intraoperative margin assessment (IMA) technique, NeuroSAFE is based on Intra-operative frozen section (IFS) of both prostate lateral sides sampled at 3-5 mm intervals. However, despite proven oncological benefit, adoption is still limited to expert centers due to a prolonged operative time, loss of integrity of the surgical specimen for definitive histopathological analysis, omission of regions (apex, base), and logistical complexity (trained uropathologist, cryostat…). With this prospective single-center project, we wish to explore the use of non-invasive optical imaging technology, HyperSpectral Imaging (HSI), as a reliable alternative for examination of the ex-vivo prostate specimen to 1. Distinguish normal versus tumor tissue at the surgical margin in fresh specimen. 2. Quantify whether HSI can be integrated into the intraoperative workflow (feasibility), thus, image acquisition time and inference time should then be evaluated.

Detailed description

Prostate cancer (Pca) is the second most frequently diagnosed cancer in men worldwide with over 1.5 million new cases annually and a median age at diagnosis of 66 years. Despite the natural history being slow and clinically insignificant in most cases, its heterogeneity and high prevalence mean it remains the second leading cause of cancer-related death worldwide. Treatment options depend on cancer stage, risk stratification and patient preferences. Radical prostatectomy (RP), which consists of the surgical removal of the prostate and the seminal vesicles, is one of the gold-standard curative treatment for localized diseases. RP techniques include open, laparoscopic, and robotic-assisted surgery. Despite recent advances in the robotic field, positive surgical margins (PSM) can occur up to 42 %. According to the International Society of Urological Pathology (ISUP), PSM is defined by tumor cells reaching the inked surgical margin of the prostatectomy specimen. PSM significantly reduces progression-free survival and is a known prognostic parameter for postoperative biochemical recurrence (BCR). Current margin assessment relies on Intraoperative Frozen Section (IFS), the current gold-standard technique is the Neurovascular Structure-Adjacent Frozen-Section Examination (NeuroSAFE). It consists of an IFS analysis of tissue adjacent to neurovascular bundles. While this neurovascular structure sparing method improves postoperative erectile function and reduces PSM, its implementation is limited due to additional operating time (45-60 minutes), increased resource demands on pathology departments, and the inability to provide a whole specimen analysis. Hyperspectral Imaging (HSI) combines conventional imaging (camera) with a spectrometer, providing both spatial and spectral information of the analyzed structures, enabling tissue differentiation based on structural properties. HSI uses reflectance spectroscopic imaging measurements, which involve a white light irradiation of the tissue and subsequently a recording of the remitted spectral intensities. The interaction between the incident light and the tissue modifies the light's spectral distribution with the reflected light containing information about the tissue composition. This technology has already been successfully applied in visceral surgery to quantify liver viability during ischemic and reperfusion phases. More recently, in Urology, HSI was successfully used as a predictive tool for early postoperative kidney graft. Another clinical application of HSI is the ability to discriminate between normal and cancerous tissue as observed in gastric, head and neck (H&N), colorectal, brain and breast cancers. Similarly, in 2024, spectral data from Diffuse Reflectance Spectroscopy (DRS) showed promising results achieving an average sensitivity of 89% with a specificity of 82%, in 59 prostatectomy specimens, confirming the potential of spectroscopy. However, DRS approach comes with limitations, more particularly in spatial orientation because it requires multiple point-based measurements across the prostate surface with the inability to evaluate the entire tissue volume. In contrast, HSI provides spatially resolved spectral data across the entire field of view, enabling comprehensive tissue mapping. In 2012, the feasibility of HSI for PCa detection was evaluated in in-vivo mice and in pathological tissue slides, achieving 93% sensitivity and 97% specificity using a support vector machine algorithm and providing an estimated AUC-ROC of 0.9485. In this context, HSI provides a unique opportunity to assess fully a specimen. However, its application for real-time margin assessment during surgery remains unexplored. Recently, a 3D pipeline that co-registers multi-angle ex-vivo HSI with volumetric histopathology was implemented, enabling voxel-wise tumor segmentation and AR visualization in H&N specimens using convolutional neural networks (CNNs). This project aims to validate HSI feasibility in ex-vivo prostate specimens. By providing a non-invasive and contactless evaluation in the operating theatre, HSI could reduce PSM rates, minimizing adjuvant radiotherapy needs and overall, improve long-term oncological outcomes. In this research project, the TIVITA® Tissue system, a CMOS (Complementary Metal Oxide Semiconductor) Push-broom Scanning Hyperspectral Camera device, designed for real-time tissue assessment, will be employed. This CE (Conformité Européenne) certified device can provide 640x480 pixel images with a spectral range of 500 to 1000 nm and a spectral resolution of 5nm. The illumination system consists in six 20 W halogen spotlights (OSRAM GmbH, Munich, Germany). Each measurement takes approximately 7 seconds with a measured area up to 20-30 cm (wide enough for a whole prostate specimen).

Interventions

PROCEDUREHyperSpectral imaging acquisition of ex-vivo prostate specimen prior formalin fixation (fresh acquisition)..

The study specific specimen handling consists in a multi-Angle HSI Image Acquisition before routine pathology. To enhance the co-registration between the two modalities, a black-ink may be applied on the ex-vivo specimen based on a MRI suspicious region of extra-prostatic extension (EPE Grade ≥ 2). This procedure follows prior robust registration work between ex-vivo HSI and histology evaluation. The identification of this region will rely on the standard pre-operative MRI review already performed as part of routine care within our prostate cancer center before each radical prostatectomy.

Sponsors

Massimo Valerio
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE

Intervention model description

Prospective exploratory pilot study

Eligibility

Sex/Gender
MALE
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Histological diagnosis (biopsy) of Pca * Prostate volume \< 100 cm3 (measured on preoperative MRI) for a smooth acquisition with the hyperspectral device * Capable of forming his own views

Exclusion criteria

* Unwilling to participate. * Inability to follow study procedures, for example due to language problems, psychological disorders, dementia, etc.

Design outcomes

Primary

MeasureTime frameDescription
Tumor extentHSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7Dice similarity coefficient between HSI-derived and histopathology spatial overlap between the HSI tumor map and co-registered histopathology

Secondary

MeasureTime frameDescription
Boundary delineationHSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7Normalized surface distance between HSI-derived andhistopathological tumor contours. Contour agreement
Region overlapHSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7Jaccard index between HSI-derived and histopathological tumor masks intersection over union
Foreground discriminationHSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7Pixel-wise sensitivity of HSI for tumor classification. Proportion of histologically tumor-positive pixels classified as tumor
Background discriminationHSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7Pixel-wise specificity of HSI for tumor classification . Proportion of histologically non-tumor pixels classified as non-tumor
Overall agreement:HSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7Pixel-wise accuracy of HSI for tumor classification. Proportion of evaluated pixels correctly classified, histopathology as reference
Clinical workflowHSI acquisition on Day 0 (day of surgery)HSI acquisition time
Computational runtimeHSI acquisition on Day 0 (day of surgery)Mean model inference time per hyperspectral image

Countries

Switzerland

Contacts

CONTACTVincent Benard, Resident
vincent.benard@hug.ch+ 41 79 553 62 68
CONTACTLaurence Zulianello, PhD
laurence.zulianello@hug.ch+ 41 22 372 79 70
PRINCIPAL_INVESTIGATORMassimo Valerio

HUG

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Sep 18, 2026