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RecruitingNCT07826858SPECTRAUpdated Sep 17, 2026

SPECTRA: Intraoperative Hyperspectral Imaging for Prostate Cancer

An interventional study of HyperSpectral imaging acquisition of ex-vivo prostate specimen prior formalin fixation (fresh acquisition).. in Localized Prostate Cancer, Positive Surgical Margin and Hyperspectral Imaging, sponsored by Massimo Valerio. Recruiting at 1 site in Switzerland. Open to male participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-17.

Sponsored by Massimo Valerio · Not applicable, Interventional, and Diagnostic

From the registry’s dates

  • Started Aug 2026; still recruiting 3 months later.

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Phase
Not applicable
Study type
Interventional
Enrollment
26
Allocation
Not applicable
Ages
18 Years and older
Sex
Male
01

Study 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.
Read the 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).

02

Conditions studied

  • Localized Prostate Cancer
  • Positive Surgical Margin
  • Hyperspectral Imaging

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Keywords

  • Radical prostatectomy
  • Localized prostate cancer
  • Positive Surgical Margins
  • HyperSpectral Imaging (HSI)
  • Hyperspectral Reflectance Imaging
  • Resection margins
03

In context

Margins of Excision

93 studies on the registry are indexed under Margins of Excision; 37 are open to participants now.

This study's planned enrollment of 26 is below the median of 84 across 64 interventional studies indexed under Margins of Excision.

Browse Margins of Excision studies →

Lead sponsor

Massimo Valerio is the lead sponsor of 2 studies on the registry; 2 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
Male
Accepts healthy volunteers
No

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • Histological diagnosis () 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

Exclusion Criteria:

  • Unwilling to participate.
  • Inability to follow study procedures, for example due to language problems, psychological disorders, dementia, etc.
Terms in these criteria, in plain language(1)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Biopsy and archival tissue

A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.

Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.

05

Study design

Phase
Not applicable
Primary purpose
Diagnostic
Allocation
Not applicable
Intervention model
Single group
Masking
None (open label)
Enrollment
26 participants (estimated)

Study arms

  • Experimental
    Participants with prostate cancer undergoing robot-assisted prostatectomy. HyperSpectral imaging.

    One visit related to the research protocol which consists in the information consultation during which the participant receives detailed oral and written information. RAPR (day 0) is performed according to standard practice and is not part of the research-specific procedure. The study is then based solely on ex-vivo analysis.

    Procedure: HyperSpectral imaging acquisition of ex-vivo prostate specimen prior formalin fixation (fresh acquisition)..

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.

06

What researchers measure

Primary outcomes

  1. Tumor extent

    Dice similarity coefficient between HSI-derived and histopathology spatial overlap between the HSI tumor map and co-registered histopathology

    Time frame: HSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7

Secondary outcomes

  1. Boundary delineation

    Normalized surface distance between HSI-derived andhistopathological tumor contours. Contour agreement

    Time frame: HSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7

  2. Region overlap

    Jaccard index between HSI-derived and histopathological tumor masks intersection over union

    Time frame: HSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7

  3. Foreground discrimination

    Pixel-wise sensitivity of HSI for tumor classification. Proportion of histologically tumor-positive pixels classified as tumor

    Time frame: HSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7

  4. Background discrimination

    Pixel-wise specificity of HSI for tumor classification . Proportion of histologically non-tumor pixels classified as non-tumor

    Time frame: HSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7

  5. Overall agreement:

    Pixel-wise accuracy of HSI for tumor classification. Proportion of evaluated pixels correctly classified, histopathology as reference

    Time frame: HSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7

  6. Clinical workflow

    HSI acquisition time

    Time frame: HSI acquisition on Day 0 (day of surgery)

  7. Computational runtime

    Mean model inference time per hyperspectral image

    Time frame: HSI acquisition on Day 0 (day of surgery)

Other outcomes

  1. Surgical margins

    Diagnostic accuracy of HSI for detection of histologically positive margins

    Time frame: HSI acquisition on Day 0 (day of surgery); reference histopathological assessment on postoperative Days 7

07

Study locations

1 of 1 sites recruiting
  • Hôpitaux Universitaires de Genève - Dept de Chirurgie - Service d'Urologie
    Geneva, Canton of Geneva 1205, Switzerland
    Recruiting
    • Vincent Benard, Resident · Contactvincent.benard@hug.ch · + 41 79 553 62 68
    • Laurence Zulianello, PhD · Contactlaurence.zulianello@hug.ch · + 41 79 553 44 15
    • Massimo Valerio, Prof Dr med · Principal investigator
    • Jean-Christophe Tille, MD · Sub investigator
    • Oliver Windisch, MD · Sub investigator
    • Michele Diana, Prof Dr med · Sub investigator
    • Doron Merkler, Prof Dr med · Sub investigator
    • Mario Kreutzfeldt, MD · Sub investigator
    • Andrew Janawczyk, Prof Dr med · Sub investigator
    • Vincent Benard, Resident · Sub investigator
    • Christian Lovis, Prof Dr med · Sub investigator
    • Mina Bjelogrlic, PhD · Sub investigator
08

References and documents

Individual participant data

Plan to share: No — Exploratory study with a small number of participants.

No publications or documents are linked to this record.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reachedSimilar studies: half by the dot; the band is the middle halfHalf of similar studies reached it by the dot; the band is the middle half, from a quarter to three quarters

Started Aug 2026 (actual). Primary completion is estimated for Aug 2027, 12 months after the start.

Of 28 interventional studies with no phase under Margins of Excision, half reached primary completion within 30 months of starting (the median), counting those still running; a quarter within 10 and three quarters within 63. This study's planned 12 months is below that.

How these figures are worked out, and which studies are left out of them.

Registered
Aug 31, 2026
First posted
Sep 17, 2026
StartActual
Aug 1, 2026
Primary completionEst.
Aug 31, 2027
CompletionEst.
Dec 31, 2027
Last update
Sep 17, 2026
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Sep 17, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT07826858
Lead sponsor
Massimo Valerio
Responsible party
Massimo Valerio (Prof Dr med, University Hospital, Geneva) — Sponsor-investigator
First posted
Sep 17, 2026
Start date
Aug 1, 2026
Primary completion
Aug 31, 2027 (estimated)
Completion
Dec 31, 2027 (estimated)
Last update
Sep 17, 2026

Study contacts

Vincent Benard, Resident
Contact
vincent.benard@hug.ch
+ 41 79 553 62 68
Laurence Zulianello, PhD
Contact
laurence.zulianello@hug.ch
+ 41 22 372 79 70
Massimo Valerio
principal investigator · HUG

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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