BACKGROUND:As the histopathology workforce continues to struggle and service demand continues to increase, it has become prudent to consider viable avenues to try to alleviate diagnostic workload burden. One such avenue is computer-based technologies (CBTs). Breast cancer (BC) is the most common malignant neoplasm in the United Kingdom and requires additional testing for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status at the time of histological diagnosis. This makes BC diagnostics a promising candidate for the application of an efficient CBT. However, for clinical acceptance, these technologies must prove that they work within a real-life diagnostic environment. OBJECTIVE:We present a study protocol for a prospective clinical service evaluation aimed to validate a UK Conformity Assessed-marked CBT's ability to provide ER, PR, and HER2 results for invasive BCs from scanned hematoxylin and eosin-stained whole slide images. METHODS:This protocol has been designed to use and mimic a preexisting digital pathology workflow within a National Health Service tertiary referral cancer center without disrupting normal patient care. Eligible cases are identified prospectively through the laboratory information management system, and their whole slide images are extracted from the clinical digital workflow. After verification of national data opt-out status and the exclusion of appropriate cases (N=400 analyzable cases), these cases are analyzed on a dedicated computer in parallel to the existing clinical workflow by a UK Conformity Assessed-marked deep learning-based CBT in a separate environment, providing results for ER, PR, and HER2 status. These results are compared to the ER, PR, and HER2 status reported on the corresponding pathology report. To evaluate the CBT's performance, a range of accepted concordance measures will be applied, including specificity, sensitivity, false-positive rate, false-negative rate, positive predictive value, and negative predictive value. Moreover, time stamps representing the duration of image analysis will also be collected. RESULTS:This study started in April 2025. There are no results to present, as this paper focuses on study design, and results have yet to be generated. As of March 2026, overall, 366 potentially analyzable cases have been collected. The anticipated end date of the study is May 2026 (400-case target). Results will be presented in a separate publication. CONCLUSIONS:This design assesses a CBT within a clinical environment while effectively eliminating any unwanted effects on patient care. This type of service evaluation provides a useful step to establish confidence in a CBT before trialing its effect on patient care. It also offers the opportunity to support interventional randomized controlled trials, health economic evaluations, and usability studies. This protocol will hopefully prove useful to others who wish to conduct a similar service evaluation at their own institution. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/76785.

Mismatch repair (MMR) deficiency occurs in 10-20% of colorectal cancer (CRC) cases, leading to microsatellite instability (MSI). Although MSI/MMR testing is critical for CRC management, high costs and long turnaround times limit testing rates and clinical utility, highlighting the need for more accessible, cost-effective alternatives. PANProfiler Colorectal (PPC) is an artificial intelligence (AI)-based biomarker test that determines MSI/MMR status directly from haematoxylin and eosin (H&E)-stained slides. We conducted a blinded, multi-centred validation to assess PPC's performance against standard testing. The study included 3,576 whole slide images from 1,243 CRC patients across three United Kingdom institutions. PPC produced definitive results for 86.55% of slides, achieving an overall agreement of 93.83%, positive agreement of 92.54%, and negative agreement of 94.02%. PPC accurately determined MSI/MMR status from routine H&E slides, offering a rapid, scalable alternative to conventional diagnostic methods.

BACKGROUND:PANProfiler Breast is a UKCA-marked, deep-learning image analysis tool. It provides oestrogen and progesterone receptor (ER/PR) status and identifies human epidermal growth factor receptor-2 (HER2) negativity from whole slide images (WSIs) of haematoxylin and eosin (H&E)-stained breast cancer (BC) tissue. This study blindly validated PANProfiler's prediction of ER/PR status and identification of HER2 negative status. MATERIALS AND METHODS:Three cohorts of WSIs of H&E-stained BC specimens were used for calibration (200 cases, 344 WSIs) and blind validation (200 cases, 348 WSIs). For the blind validation, PANProfiler analysed WSIs to provide results for ER, PR ("Positive," "Negative," or "Indeterminate") and HER2 ("Negative" or "Indeterminate"). These were compared to the corresponding pathology reports. To discern PANProfiler's performance, concordance and other metrics were calculated, including test replacement rate (TRR) (cases PANProfiler produced a definitive result for) and complete test replacement rate (CTTR) (cases with definitive results for all markers). RESULTS:Following blind validation, concordance for ER and PR status across the cohorts was 90%-93% and 86%-91%, respectively. The TRR for ER was 70%-84% and 55%-84% for PR. For HER2 negative cases, concordance across cohorts was 91%-100%, with a TRR ranging from 22% to 27%. CTTRs for the cohorts were between 18% and 20%. CONCLUSION:PANProfiler Breast showed high concordance for ER and PR status and identified HER2 negativity from WSIs of H&E-stained BCs. For HER2 negativity, whilst the TRR was lower than that of ER and PR, the high level of concordance indicated its reliability in identifying negative cases.

44 Background: Testing for microsatellite instability (MSI) or mismatch repair deficiency (dMMR) is part of the diagnosis and clinical management of patients with colorectal cancer (CRC). Healthcare services recommend MSI or dMMR testing for all CRC patients to guide therapeutic choices and assist in identifying Lynch Syndrome. However, in clinical practice, high costs and the demand for timely test results, combined with the rising prevalence of CRC and a shrinking pathology workforce, present a barrier to universal adoption. This highlights the need for rapid and affordable alternatives. PANProfiler CRC (PPC) is a deep learning-based solution for detecting MSI/dMMR in CRC tumors that only requires whole slide images (WSIs) of haematoxylin and eosin (H&E)-stained tissue to provide test results. Using only WSIs, PPC offers an efficient alternative to standard testing. This study evaluates PPC's performance in a multi-site blinded setting. Methods: Blinded validation was performed using 3246 WSIs of H&E-stained CRC specimens. PPC provided outputs as "Stable", "Unstable", or "Indeterminate", with "Unstable" indicating dMMR or MSI-High, and "Stable" indicating proficient mismatch repair or non-MSI-High. "Indeterminate" was returned when PPC did not have a definitive result. PPC was evaluated by comparison to standard MSI/dMMR tests. Validation data spanned three cohorts from two sites (Table). St James’s University Hospital (SJUH), Leeds, UK, supplied Cohorts 1 and 2; Cohort 3 was sourced from Wales Cancer Biobank (WCB), UK. Blinded analysis was performed at SJUH. Results: Results are given (Table). PPC demonstrated an overall percent agreement of 93.91%, a positive percent agreement of 92.17%, and a negative percent agreement of 94.15%, returning a definitive result for 88.05% of WSIs. Conclusions: This real-world, multi-site, blinded validation study demonstrates PPC’s remarkable performance, comparable to standard tests for detecting MSI/dMMR in CRC, with high test replacement rates. In the clinical setting, PPC could significantly accelerate testing and enable timely delivery of stratified treatment plans. This accurate and cost-effective diagnostic solution promises to revolutionize MSI/dMMR testing in CRC. Blinded validation results of PPC with confidence intervals (CI) at 95%. Site Cohort Sample Size (Unstable; Stable) Overall Percent Agreement % (CI) Positive Percent Agreement % (CI) Negative Percent Agreement %(CI) Test Replacement Rate % SJUH 1 488 (78; 410) 92.79 (89.86-95.08) 90.16 (79.81-96.30) 93.24(90.11-95.62) 85.25 SJUH 2 2704 (318; 2386) 94.31(93.31-95.21) 92.31(88.48-95.18) 94.57(93.52-95.50) 88.42 WCB 3 54 (11; 43) 84.31 (71.41-92.98) 100.00(71.51-100.00) 80.00(64.35-90.95) 94.44 All 3246 (407; 2839) 93.91 (92.97-94.76) 92.17(88.82-94.78) 94.15(93.16-95.04) 88.05

e15718 Background: Microsatellite instability (MSI) and mismatch repair (MMR) testing is critical for guiding therapeutic decisions in colorectal cancer (CRC). Despite their clinical importance, routine MSI/MMR testing faces significant challenges, including high costs, long turnaround times, and pathology workforce shortages. Artificial intelligence (AI) offers opportunities to overcome these barriers through data-driven solutions. To maximize the clinical utility and performance of AI-based approaches, it is crucial to identify and analyze the key predictors of MSI/MMR status. Such analysis not only aids in developing more accurate predictive models, but also supports efforts to adapt diagnostic tools to diverse patient populations. To this end, we conducted a comprehensive study with a real-world clinical dataset of retrospective CRC cases to identify the critical factors associated with MSI/MMR status. Methods: Clinical and histopathological data from 800 CRC cases at St James’s University Hospital (UK) were analyzed using a random forest (RF) classifier to identify the most important predictors for determining the overall MSI/MMR status. MSI/MMR testing was done as part of routine clinical care, with 11.9% of cases classified as MMR-deficient/MSI-high (n = 95). Normalized importance values were computed for each feature to quantify their contribution. Pairwise correlation analysis was conducted using Cramer's V and Chi-squared tests to evaluate interdependencies among features. The cohort included 308 patients (38.5%) under 65 years of age and 492 patients (61.5%) aged 65 or older, with a higher proportion of males (n = 453, 56.6%) compared to females (n = 347, 43.4%). The majority of patients were diagnosed with Stage II and III cancers (n = 557, 69.6%) and had tumors graded as moderately differentiated (n = 620, 78.6%). The primary tumor site was the colon (n = 528, 66.0%) and the most common histological subtype was adenocarcinoma (n = 686, 85.8%). Results: The classifier identified age as the most influential feature associated with MSI/MMR status, with an importance of 52.9%. Stage and grade were the next most significant contributors, accounting for 17.0% and 12.2%, respectively. Histological subtype and tumor site were less influential, with contributions of 7.8% and 6.0%, respectively. Gender was the least impactful factor, with an importance of 4.1%. Pairwise correlation analysis showed weak associations among individual features, with all Cramer's V values below 0.26 and Chi-squared tests indicating statistical significance (p < 0.001). Conclusions: Our analysis highlights the role of age, stage, and grade in determining MSI/MMR status, with minimal interdependencies among features. These results provide valuable insights for refining predictive models and advancing the development of reliable, generalizable diagnostic tools for diverse CRC patient populations.

Abstract Background: Molecular profiling of estrogen and progesterone receptors (ER/PR/Her2) is performed for all malignant breast cancers to inform the choice of targeted therapy. Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results. We evaluate PANProfiler Breast(PPB), a UKCA/CE- IVDD marked deep learning (DL)-based image analysis software, on multiple sites to determine if the majority of ER/PR assays can be replaced, relying only on routinely-used H&E-stained whole slide images. Methods: PPB was trained and validated on 5126/4619 WSIs from 5 sites to identify the ER/PR status defined by IHC assays graded in alignment with ASCO/RCPATH guidelines from five different sites in the UK were used for training and validation. The performance is evaluated separately for each site with 3-fold cross-validation, mimicking real-world distribution. Results: For ER, with a class ratio (CR) of approximately 4:1, we measure a sensitivity, specificity, and accuracy of 95.5%(±1.5%), 47.5%(±15.1%) and 88.3%(±0.6%) averaged over all sites, reaching up to 97.40%, 67.20%, and 89.30% respectively. For PR (CR approx. 3:1), the averaged sensitivity, specificity, and accuracy are 92.2%(±8.4%), 53.10%(±20.8%), and 86.6%(±2.4%), reaching up to 99.1%, 81.7%, and 88.8%, respectively. The software's performance is comparable to current SoC antibody performance in common ER/PR CDx Assays from Dako, Leica, and Roche, which have sensitivities of 98.5%(±1.3%) and specificities of 38.6%(±6.3%) for ER and sensitivities of 96.9%(±0.6%) and specificities of 23.4%(±1.4%) for PR. Performance was robust to specimen and scanner types, with accuracies of 87.6% (ER, only biopsies), 88.2% (ER, only resections), 88.5% (ER mixed types), 83.9% (PR, only resections) and 87.3% (mixed types). Accuracy across scanners varied by a standard deviation of 0.3%/1.0% for ER/PR respectively. Conclusions: We demonstrate the robustness of a DL-based ER/PR profiling method in breast cancer using only H&E-stained WSIs. This multi-site validation study is the first-of-its-kind for such an approach using real-world clinical data. Our solution could facilitate fast, accurate, and systemic screening of patients for targeted treatments if integrated into routine pathological workflows. Citation Format: Salim Arslan, Adrian Bazaga, Gareth Bryson, Oscar Carlos, Andre Geraldes, David Harrison, Alastair Ironside, Jakob Kather, Ali Khurram, David Leff, Debapriya Mehrotra, Foivos Ntelemis, John Nyonyintono, Julian Schmidt, Shikha Singhal, In Hwa Um, Steffen Wolf, Pahini Pandya. Multi-site validation of a deep learning solution for ER/PR profiling of breast cancer from H&E-stained pathology slides [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-05.

Molecular profiling of the human epidermal growth factor receptor 2 (HER2) is performed for all malignant breast cancers to inform the choice of HER2-targeted therapy. IHC and ISH are performed to establish HER2 status in standard clinical care. These tests are not only expensive and time-consuming, but also account for most of the turnaround time for diagnosis and require expert interpretation. Here, we demonstrate the effectiveness of PANProfiler Breast (HER2 Negative), a UKCA-marked deep learning (DL)-based image analysis software for HER2 profiling from whole slide images (WSIs) of routinely-used H&E-stained pathology slides. A DL model was trained and validated on 1684 WSIs from two datasets to identify HER2-negative cases defined by IHC0, IHC1 or IHC2+/ISH-. A total of 2310 H&E images of breast cancer samples from five different sites in the UK and US were used for external validation. These images were acquired with four different scanners with each set containing a varying proportion of biopsies and resections. The model was evaluated separately for each site with 3-fold cross-validation and results were aggregated across folds. The performance was measured in comparison to HER2 status acquired via IHC and ISH in accordance with RCPath & CAP guidelines. Incidence rate for HER2-positive cases varied between 9-15% in the UK sites and was 64% for the US cohort. Overall accuracy across all sites was 93.06% (± 8.28%), reaching up to 97.99%. An average false negative rate (discordance to wet-lab assay) of 9.27% (± 0.47%) was achieved with a 100% specificity for all datasets. Performance was robust to specimen/scanner types, with accuracies of 96.46%, 95.39%, and 91.15% achieved in sites that contained only biopsies, only resections, and mixed types. Accuracy across scanners varied by a standard deviation of 9.25%. We demonstrate the robustness of a DL-based HER2 profiling method in breast cancer utilising only H&E-stained WSIs. This multi-site validation study is the first-of its kind for such an approach using real-world clinical data. Our solution could facilitate fast, accurate, and systemic screening of patients for targeted treatments if integrated within the routine pathological workflows.

562 Background: Newly diagnosed breast cancer specimens are routinely tested for oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2) status. This requires additional immunohistochemistry (IHC) +/- in situ hybridisation (ISH) assessment. This can increase laboratory/pathologist workloads and turnaround times. A computer-based approach could help to alleviate these issues. PANProfiler Breast is a deep-learning solution designed to predict ER/PR status and detect HER2 negative cases from whole slide images (WSIs) of haematoxylin and eosin (H&E)-stained breast cancer tissue. Here we present a blind validation of PANProfiler Breast. Methods: Three cohorts of H&E-stained archival breast cancers (400 cases, 692 WSIs) from St James’s University Hospital, Leeds, UK, were assigned for calibration (200 cases, 344 WSIs) and blind validation (200 cases, 348 WSIs). Slides were scanned at 40x magnification on an Aperio GT450 (Leica, Illinois, USA) scanner. PANProfiler returned “Positive”, “Negative” or “Indeterminate” for ER and PR, and “Negative” or “Indeterminate” for HER2. Results were compared to the ER/PR/HER2 status given in the corresponding pathology report. For calibration and blind validation, the WSI(s) corresponding to the IHC/ISH testing block was used for analysis. One case with missing HER2 status was excluded from HER2 analyses. Confusion matrices enabled analysis of concordance. While a conventional Positive/Negative confusion matrix was used for ER/PR, “Negative” results were used as the reference Positive event for HER2. Results: Case concordance for ER and PR status across the three cohorts ranged from 89.74-92.86% and 85.71-90.91% respectively. Additionally, sensitivity for ER ranged from 89.19-100%, with specificity ranging between 62.50-100%. For PR, sensitivity was between 87.50% and 93.48% and specificity between 75.00% and 83.33%. False negative rates for both ER and PR were between 0-10.81% and 6.52-12.50% respectively. Regarding HER2, sensitivity was relatively low, ranging between 30.77% and 37.04%. However, over 90% concordance was seen across all cohorts for cases PANProfiler predicted as HER2 “Negative”. Specificity and positive predictive value were also over 90% throughout the three cohorts. Additionally, false positive rates were 0.00%, 4.35% and 9.52% for HER2 across these same three cohorts. Test replacement rates varied amongst the cohorts with 70-84% for ER, 55-84% for PR and 22-27% for HER2. Conclusions: This blind validation of PANProfiler Breast shows impressive levels of performance for predicting ER and PR status in breast cancer WSIs. For HER2, the technology demonstrated a striking level of confidence for cases predicted as HER2 “Negative”. Further development and analysis are warranted to increase the number of HER2 negative BC WSIs that PANProfiler is able to identify.

Background The objective of this comprehensive pan-cancer study is to evaluate the potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from hematoxylin and eosin (H&E)-stained whole slide images. Methods A total of 12,093 DL models predicting 4031 multi-omic biomarkers across 32 cancer types were trained and validated. The study included a broad range of genetic, transcriptomic, and proteomic biomarkers, as well as established prognostic markers, molecular subtypes, and clinical outcomes. Results Here we show that 50% of the models achieve an area under the curve (AUC) of 0.644 or higher. The observed AUC for 25% of the models is at least 0.719 and exceeds 0.834 for the top 5%. Molecular profiling with image-based histomorphological features is generally considered feasible for most of the investigated biomarkers and across different cancer types. The performance appears to be independent of tumor purity, sample size, and class ratio (prevalence), suggesting a degree of inherent predictability in histomorphology. Conclusions The results demonstrate that DL holds promise to predict a wide range of biomarkers across the omics spectrum using only H&E-stained histological slides of solid tumors. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.

We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and eosin (H&E)-stained whole slide image (WSI) using deep learning and standard evaluation measures throughout a systematic study. A total of 13,443 deep learning (DL) models predicting 4,481 multi-omic biomarkers across 32 cancer types were trained and validated. The investigated biomarkers included genetic mutations, transcriptomic (mRNA) and proteomic under- and over-expression status, metabolomic pathways, established markers relevant for prognosis, including gene expression signatures, molecular subtypes, clinical outcomes and response to treatment. Overall, we established the general feasibility of predicting multi-omic markers across solid cancer types, where 50% of the models could predict biomarkers with the area under the curve (AUC) of more than 0.633 (with 25% of the models having AUC larger than 0.711). Aggregating across the omic types, our deep learning models achieved the following performance: mean AUC of 0.634 ±0.117 in predicting driver SNV mutations; 0.637 ±0.108 for over-/under-expression of transcriptomic genes; 0.666 ±0.108 for over-/under-expression of proteomes; 0.564 ±0.081 for metabolomic pathways; 0.653 ±0.097 for gene signatures and molecular subtypes; 0.742 ±0.120 for standard of care biomarkers; and 0.671 ±0.120 for clinical outcomes and treatment responses. The biomarkers were shown to be detectable from routine histology images across all investigated cancer types, with aggregate mean AUC exceeding 0.62 in almost all cancers. In addition, we observed that predictability is reproducible within-marker and less dependent on sample size and positivity ratio, indicating a degree of true predictability inherent to the biomarker itself.

We present a public validation of PANProfiler (ER, PR, HER2), an in-vitro medical device (IVD) that predicts the qualitative status of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) by analysing the hematoxylin and eosin (H&E)-stained tissue scan. In public validation on 648 (ER), 648 (PR) and 560 (HER2) unseen cases with known biomarker status, the device achieves an accuracy of 87% (ER), 83% (PR) and 87% (HER2). The validation offers early evidence of the ability to predict clinically relevant breast biomarkers from an H&E slide in a relevant clinical setting.

We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and eosin (H&E)-stained whole slide images (WSI) using deep learning (DL) throughout a systematic study. A total of 13,443 DL models predicting 4,481 multi-omic biomarkers across 32 cancer types were trained and validated. The investigated biomarkers included a broad range of genetic, transcriptomic, proteomic, and metabolic alterations, as well as established markers relevant for prognosis, molecular subtypes and clinical outcomes. Overall, we found that DL can predict multi-omic biomarkers directly from routine histology images across solid cancer types, with 50% of the models performing at an area under the curve (AUC) of more than 0.633 (with 25% of the models having an AUC larger than 0.711). A wide range of biomarkers were detectable from routine histology images across all investigated cancer types, with a mean AUC of at least 0.62 in almost all malignancies. Strikingly, we observed that biomarker predictability was mostly consistent and not dependent on sample size and class ratio, suggesting a degree of true predictability inherent in histomorphology. Together, the results of our study show the potential of DL to predict a multitude of biomarkers across the omics spectrum using only routine slides. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.

Fiber-coupled laser tools combine the precision of lasers with the flexibility of optical fibers. They have been used in surgical procedures, such as transoral microsurgery, to perform precise incisions in delicate structures that are often difficult to reach with other tools. Unfortunately, the performance of fiber tools is not akin to that of traditional laser systems. Prioritizing miniaturization, these tools typically use no optics and ablate tissue in near-contact mode, which induces significant level of tissue carbonization. To avoid this problem, we have developed a focus control system designed for fiber-coupled laser tools based on a microfabricated varifocal mirror. We have demonstrated the efficiency of the system by adjusting the focusing of a CO 2 laser beam when ablating plaster block targets while varying the distance to the target within the range of 10mm to 25mm. The proposed system was able to ablate uniform lines, with an average width of 420 $\mu \text{m}$ and showed higher precision than fixed focus and bare fiber systems.

Endoscopic laser tools have been recently proposed in order to overcome the limitations of state-of-the-art laser tools, by integrating fiber-coupled lasers into flexible endoscopic systems. One of the main challenges in designing such endoscopic tools consists in the focusing of the laser, that requires to be frequently adjusted reducing the reliability of the system and increasing surgeons’ mental workload. To avoid these problems, compact auto-focusing tools have been recently developed, taking advantage of MEMS varifocal mirrors (VM) to allow integration with endoscopic tools. In this paper, we integrate such VM-based tool with a distance sensing algorithm based on 3D surface reconstruction to achieve a complete autofocusing system. We evaluate the performance of the proposed integrated system by ablating lines on plaster block targets at variable distance and comparing the obtained ablation depth and width with that of a fixed focus system. Preliminary results show that the proposed system is able to keep the laser in focus resulting in uniform ablation lines for distance ranges from 14mm to 22mm.

Laser microsurgery is the current gold standard surgical technique for the treatment of selected diseases in delicate organs such as the larynx. However, the operations require large surgical expertise and dexterity, and face significant limitations imposed by available technology, such as the requirement for direct line of sight to the surgical field, restricted access, and direct manual control of the surgical instruments. To change this status quo, the European project μRALP pioneered research towards a complete redesign of current laser microsurgery systems, focusing on the development of robotic micro-technologies to enable endoscopic operations. This has fostered awareness and interest in this field, which presents a unique set of needs, requirements and constraints, leading to research and technological developments beyond μRALP and its research consortium. This paper reviews the achievements and key contributions of such research, providing an overview of the current state of the art in robot-assisted endoscopic laser microsurgery. The primary target application considered is phonomicrosurgery, which is a representative use case involving highly challenging microsurgical techniques for the treatment of glottic diseases. The paper starts by presenting the motivations and rationale for endoscopic laser microsurgery, which leads to the introduction of robotics as an enabling technology for improved surgical field accessibility, visualization and management. Then, research goals, achievements, and current state of different technologies that can build-up to an effective robotic system for endoscopic laser microsurgery are presented. This includes research in micro-robotic laser steering, flexible robotic endoscopes, augmented imaging, assistive surgeon-robot interfaces, and cognitive surgical systems. Innovations in each of these areas are shown to provide sizable progress towards more precise, safer and higher quality endoscopic laser microsurgeries. Yet, major impact is really expected from the full integration of such individual contributions into a complete clinical surgical robotic system, as illustrated in the end of this paper with a description of preliminary cadaver trials conducted with the integrated μRALP system. Overall, the contribution of this paper lays in outlining the current state of the art and open challenges in the area of robot-assisted endoscopic laser microsurgery, which has important clinical applications even beyond laryngology.

Letters1 December 2020Operating From a Distance: Robotic Vocal Cord 5G Telesurgery on a CadaverAlperen Acemoglu, PhD, Giorgio Peretti, MD, Matteo Trimarchi, MD, Juljana Hysenbelli, MS, Jan Krieglstein, MS, Andre Geraldes, PhD, Nikhil Deshpande, PhD, Pierre Marie Vincent Ceysens, MS, Darwin Gordon Caldwell, PhD, Marco Delsanto, MS, Ottavia Barboni, MM, Tommaso Vio, MS, Sabrina Baggioni, MM, Alessandro Vinciguerra, MD, Alberto Sanna, Elettra Oleari, Andrea Luigi Camillo Carobbio, MD, Luca Guastini, MD, Francesco Mora, MD, and Leonardo S. Mattos, PhDAlperen Acemoglu, PhDIstituto Italiano di Tecnologia, Genoa, Italy (A.A., J.K., A.G., N.D., P.M.C., D.G.C., L.S.M.), Giorgio Peretti, MDUniversity of Genoa – IRCCS San Martino Hospital, Genoa, Italy (G.P., A.L.C., L.G., F.M.), Matteo Trimarchi, MDUniversity Vita Salute Milano – IRCCS San Raffaele Hospital, Milan, Italy (M.T., A.V., A.S., E.O.), Juljana Hysenbelli, MSVodafone Italia, Milan, Italy (J.H., M.D., O.B., T.V., S.B.), Jan Krieglstein, MSIstituto Italiano di Tecnologia, Genoa, Italy (A.A., J.K., A.G., N.D., P.M.C., D.G.C., L.S.M.), Andre Geraldes, PhDIstituto Italiano di Tecnologia, Genoa, Italy (A.A., J.K., A.G., N.D., P.M.C., D.G.C., L.S.M.), Nikhil Deshpande, PhDIstituto Italiano di Tecnologia, Genoa, Italy (A.A., J.K., A.G., N.D., P.M.C., D.G.C., L.S.M.), Pierre Marie Vincent Ceysens, MSIstituto Italiano di Tecnologia, Genoa, Italy (A.A., J.K., A.G., N.D., P.M.C., D.G.C., L.S.M.), Darwin Gordon Caldwell, PhDIstituto Italiano di Tecnologia, Genoa, Italy (A.A., J.K., A.G., N.D., P.M.C., D.G.C., L.S.M.), Marco Delsanto, MSVodafone Italia, Milan, Italy (J.H., M.D., O.B., T.V., S.B.), Ottavia Barboni, MMVodafone Italia, Milan, Italy (J.H., M.D., O.B., T.V., S.B.), Tommaso Vio, MSVodafone Italia, Milan, Italy (J.H., M.D., O.B., T.V., S.B.), Sabrina Baggioni, MMVodafone Italia, Milan, Italy (J.H., M.D., O.B., T.V., S.B.), Alessandro Vinciguerra, MDUniversity Vita Salute Milano – IRCCS San Raffaele Hospital, Milan, Italy (M.T., A.V., A.S., E.O.), Alberto SannaUniversity Vita Salute Milano – IRCCS San Raffaele Hospital, Milan, Italy (M.T., A.V., A.S., E.O.), Elettra OleariUniversity Vita Salute Milano – IRCCS San Raffaele Hospital, Milan, Italy (M.T., A.V., A.S., E.O.), Andrea Luigi Camillo Carobbio, MDUniversity of Genoa – IRCCS San Martino Hospital, Genoa, Italy (G.P., A.L.C., L.G., F.M.), Luca Guastini, MDUniversity of Genoa – IRCCS San Martino Hospital, Genoa, Italy (G.P., A.L.C., L.G., F.M.), Francesco Mora, MDUniversity of Genoa – IRCCS San Martino Hospital, Genoa, Italy (G.P., A.L.C., L.G., F.M.), and Leonardo S. Mattos, PhDIstituto Italiano di Tecnologia, Genoa, Italy (A.A., J.K., A.G., N.D., P.M.C., D.G.C., L.S.M.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M20-0418 Annals Author Insight Video - Operating From a Distance: Robotic Vocal Cord 5G Telesurgery on a Cadaver This video offers additional insight into the article, "Operating From a Distance: Robotic Vocal Cord 5G Telesurgery on a Cadaver." (Duration 1:53) SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Background: The first telesurgery involving a human patient was done in 2001 (1). The patient, located in Strasbourg, France, had a laparoscopic cholecystectomy done by a surgeon in New York. This pioneering experience showed the potential of telehealth technology, but safe, reliable reproduction of this feat proved problematic for many years because of the limited availability of surgical robots and the lack of fast and reliable network connections. Now, however, surgical robots are becoming increasingly common and accepted in operating rooms, and the next generation of mobile networks (5G) is quickly becoming a reality, bringing ultrafast, stable, and reliable transmission ...References1. Marescaux J. [Code name: "Lindbergh operation"] [Editorial]. Ann Chir. 2002;127:2-4. [PMID: 11833301] CrossrefMedlineGoogle Scholar2. Lacy AM, Bravo R, Otero-Piñeiro AM, et al. 5G-assisted telementored surgery. Br J Surg. 2019;106:1576-1579. [PMID: 31483054] CrossrefMedlineGoogle Scholar3. Jell A, Vogel T, Ostler D, et al. 5th-generation mobile communication: data highway for surgery 4.0. Surg Technol Int. 2019;35:36-42. [PMID: 31694061] MedlineGoogle Scholar4. Remacle M, Van Haverbeke C, Eckel H, et al. Proposal for revision of the European Laryngological Society classification of endoscopic cordectomies. Eur Arch Otorhinolaryngol. 2007;264:499-504. [PMID: 17377801] CrossrefMedlineGoogle Scholar5. Acemoglu A, Deshpande N, Lee J, et al. Proceedings of the 19th International Conference on Advanced Robotics, Belo Horizonte, Brazil, 2–6 December 2019. Institute of Electrical and Electronics Engineers; 2019:641-6. Google Scholar Author, Article, and Disclosure InformationAffiliations: Istituto Italiano di Tecnologia, Genoa, Italy (A.A., J.K., A.G., N.D., P.M.C., D.G.C., L.S.M.)University of Genoa – IRCCS San Martino Hospital, Genoa, Italy (G.P., A.L.C., L.G., F.M.)University Vita Salute Milano – IRCCS San Raffaele Hospital, Milan, Italy (M.T., A.V., A.S., E.O.)Vodafone Italia, Milan, Italy (J.H., M.D., O.B., T.V., S.B.)Financial Support: This research was not funded by any specific grant and did not involve any type of reimbursement. All partners worked with their own internal resources.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-0418.Corresponding Author: Leonardo S. Mattos, PhD, Istituto Italiano di Tecnologia, Via Morego 30, Genoa, 16163 Italy; e-mail, leonardo.[email protected]it.This article was published at Annals.org on 14 July 2020. PreviousarticleNextarticle Advertisement Annals Author Insight Video - Operating From a Distance: Robotic Vocal Cord 5G Telesurgery on a Cadaver This video offers additional insight into the article, "Operating From a Distance: Robotic Vocal Cord 5G Telesurgery on a Cadaver." (Duration 1:53) FiguresReferencesRelatedDetails Metrics Cited byVR-Based Immersive Service Management in B5G Mobile Systems: A UAV Command and Control Use CaseRemote orthopedic robotic surgery: make fracture treatment no longer limited by geographyReview of the development and prospect of telemedicineGastroenterology in the Metaverse: The dawn of a new era?A Smarter Health through the Internet of Surgical ThingsRemote telesurgery in humans: a systematic reviewApplication of deterministic networking for reducing network delay in urological telesurgery: A retrospective studyCommunication Requirements in 5G-Enabled Healthcare Applications: Review and ConsiderationsThe Role of Virtual Reality, Telesurgery, and Teleproctoring in Robotic SurgeryEthical and Legal Challenges of Telemedicine Implementation in Rural AreasEducational value of surgical telementoring5G mobile communication applications for surgery: An overview of the latest literatureTechnology and TelemedicineTechnology and Telemedicine5G Robotic Telesurgery: Remote Transoral Laser Microsurgeries on a Cadaver 1 December 2020Volume 173, Issue 11Page: 940-941KeywordsComputersDisclosureHealth economicsMean effective concentrationMedical servicesPatient advocacyResearch laboratoriesSurgeonsSurgeryTelemedicine ePublished: 14 July 2020 Issue Published: 1 December 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. 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Endoscopic laser surgery is a minimally invasive procedure in which a fiber laser tool is used to perform precise incisions in soft tissue. Although the precision of such incisions depends on the proper focusing of the laser, endoscopic laser tools use no optics at all, due to the limited space in the endoscopic system. Instead, they rely on placing the tip of the fiber in direct contact with the tissue, which often leads to tissue carbonization. To solve this problem, we developed a compact auto-focusing system based on a MEMS varifocal mirror. The proposed system is able to ensure the focusing of the laser by controlling the deflection of the varifocal mirror using hydraulic actuation. Validation experiments showed that the system is able to keep the variation of the laser spot diameter under 3% for a distance range between 12.15 and 52.15 mm.