Tumor Margin Assessment Research Articles

Endoscopic In Vivo Hyperspectral Imaging for Head and Neck Tumor Surgeries Using a Medically Approved CE-Certified Camera with Rapid Visualization During Surgery.

Background: In vivo visualization of malignant tumors remains the main challenge during head and neck cancer surgery. This can result in inadequate tumor margin assessment and incomplete tumor resection, adversely affecting patient outcomes. Hyperspectral imaging (HSI) is a promising approach to address this issue. However, its application in surgery has been limited by the lack of medically approved HSI devices compliant with MDR regulations, as well as challenges regarding the integration into the surgical workflow. Methods: In this feasibility study, we employed endoscopic HSI during surgery to visualize the tumor sites of 12 head and neck cancer patients. We optimized the HSI workflow to minimize time required during surgery and to reduce the adaptation period needed for surgeons to adjust to the new workflow. Additionally, we implemented data processing to enable real-time classification and visualization of HSI within the intraoperative setting. HSI evaluation was conducted using principal component analysis and k-means clustering, with this clustering validated through comparison with expert annotations. Results: Our complete HSI workflow requires two to three minutes, with each HSI measurement-including evaluation and visualization-taking less than 10 s, achieving an accuracy of 79%, sensitivity of 72%, and specificity of 84%. Medical personnel became proficient with the HSI system after two surgeries. Conclusions: This study presents an HSI workflow for in vivo tissue differentiation during head and neck cancer surgery, providing accurate and visually accessible results within minimal time. This approach enhances the in vivo evaluation of tumor margins, leading to more clear margins and, consequently, improved patient outcomes.

Autofluorescence imaging guided needle-type Raman spectroscopy system for breast tumor margin assessment.

A trajectory-tracked, near-infrared autofluorescence imaging guided, biochemical signature-projected needle-type Raman spectroscopy (TNBN-RS) system integrated on a medical cart was developed for rapid wide-field breast tissue stratification. A wide-field (10 × 10 cm2) near-infrared autofluorescence (NIRAF) imaging subsystem was developed for gross stratification of breast tissue types based on higher NIRAF intensity associated with breast cancer, followed by projection of NIRAF-identified breast tumor margins onto the tissue of interest with a compact projector. Raman spectra were further acquired from the NIRAF projected regions for confirmed margin assessment using a needle-type Raman probe equipped with color camera-based probe trajectory tracking. The trajectory of the Raman probe and the accompanying RS biochemical signature-based margin assessment were instantly projected. A unique field of view (FOV) calibration method was proposed to calibrate the TNBN-RS FOVs, resulting in a projection accuracy of <2 mm. A graphical user interface (GUI) was developed in C# for system control, real-time processing and display of NIRAF images, Raman spectra, and projection of their results. The performance of the TNBN-RS system was validated on an ex vivo breast tissue, demonstrating its potential for rapid intraoperative breast tumor margin assessment.

Intraoperative margin assessment with near real time pathology during partial gland ablation of prostate cancer: A feasibility study

BackgroundIn-field or in-margin recurrence after partial gland cryosurgical ablation (PGCA) of prostate cancer (PCa) remains a limitation of the paradigm. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real time, label-free, high-resolution microscopic images of unprocessed, un-sectioned tissue which can be interpreted by humans or artificial intelligence (AI). We evaluated surgical team and AI interpretation of SRH for real-time pathologic feedback in the planning and treatment of PCa with PGCA. MethodsAbout 12 participants underwent prostate mapping biopsies during PGCA of their PCa between January and June 2022. Prostate biopsies were immediately scanned in a SRH microscope at 20 microns depth using 2 Raman shifts to create SRH images which were interpreted by the surgical team intraoperatively to guide PGCA, and retrospectively assessed by AI. The cores were then processed, hematoxylin and eosin stained as per normal pathologic protocols and used for ground truth pathologic assessment. ResultsSurgical team interpretation of SRH intraoperatively revealed 98.1% accuracy, 100% sensitivity, 97.3% specificity for identification of PCa, while AI showed a 97.9% accuracy, 100% sensitivity and 97.5% specificity for identification of clinically significant PCa. 3 participants’ PGCA treatments were modified after SRH visualized PCa adjacent to an expected MRI predicted tumor margin or at an untreated cryosurgical margin. ConclusionSRH allows for accurate rapid identification of PCa in PB by a surgical team interpretation or AI. PCa tumor mapping and margin assessment during PGCA appears to be feasible and accurate. Further studies evaluating impact on clinical outcomes are warranted.

Vertical Ex Vivo Dermoscopy in Assessment of Malignant Skin Lesions.

The role of vertical ex vivo dermoscopy relevant to clinical diagnosis has not been investigated yet. Study objectives were defining, describing, and determining the importance of the structures visible using vertical ex vivo dermoscopy in the diagnosis of malignant skin lesions, as well as determining their accuracy in the assessment of tumor margins. A prospective, descriptive study was conducted in two University centers. Digital images of completely excised skin lesions, fixed in formalin, before histopathological diagnosis were used for analysis. BCCs had the most diverse dermoscopic presentation on the vertical section, while SCCs showed a similar presentation in most cases. Vertical dermoscopy of thin melanomas was almost identical, unlike nodular melanomas. Thickness accuracy assessed by dermatologist was 0.753 for BCC, 0.810 for SCC, and 0.800 for melanomas, whereas assessment by pathologist was 0.654, 0.752, and 0.833, respectively. The accuracy of tumor width assessment was 0.819 for BCCs, 0.867 for SCCs and 1.000 for melanoma as estimated by a Dermatologist. Interobserver agreement was 0.71 for BCC, 0.799 for SCC and 0.832 for melanomas. Vertical ex vivo dermoscopy may contribute to the distinction between BCCs, SCCs, and melanomas. Moreover, regardless of the doctor's specialty, it enables a good assessment of the tumor's margins.

Infrared Fluorescence-guided Surgery for Tumor and Metastatic Lymph Node Detection in Head and Neck Cancer.

In patients with head and neck cancer (HNC), surgical removal of cancerous tissue presents the best overall survival rate. However, failure to obtain negative margins during resection has remained a steady concern over the past 3 decades. The need for improved tumor removal and margin assessment presents an ongoing concern for the field. While near-infrared agents have long been used in imaging, investigation of these agents for use in HNC imaging has dramatically expanded in the past decade. Targeted tracers for use in primary and metastatic lymph node detection are of particular interest, with panitumumab-IRDye800 as a major candidate in current studies. This review aims to provide an overview of intraoperative near-infrared fluorescence-guided surgery techniques used in the clinical detection of malignant tissue and sentinel lymph nodes in HNC, highlighting current applications, limitations, and future directions for use of this technology within the field. Keywords: Molecular Imaging-Cancer, Fluorescence © RSNA, 2024.

Quantitative MRI approach in the preoperative evaluation of a rare large-mass abdominal GIST tumor

Gastrointestinal stromal tumors (GISTs) are rare cancers originating from the gastrointestinal tract's interstitial cells of Cajal. This study explores the utility of quantitative Magnetic Resonance Imaging (MRI), particularly volumetric lesion segmentation, in the preoperative evaluation of large-mass abdominal GISTs. MRI offers high-resolution imaging without ionizing radiation, which is crucial for oncology patients requiring repeated scans. Advanced semi-automatic segmentation techniques were employed in this study from different MRI sequences to enhance precision in tumor boundary delineation and volumetric analysis, and aid personalized surgical planning. In addition, this study demonstrates that using different clinical imaging protocols and comparing volumetric data from CT and various MRI sequences yields consistent results with minimal volumetric error. Finally, integrating MRI data into real-time surgical navigation systems underscored the synergy between radiology, surgical oncology, and computer science, improving tumor margin assessment and reducing recurrence risk. This approach highlights a pivotal shift towards technology-enhanced healthcare, promising better patient outcomes.

Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment.

The assessment information of tumor margins is extremely important for the success of the breast cancer surgery and whether the patient undergoes a second operation. However, conducting surgical margin assessments is a time-consuming task that requires pathology-related skills and equipment, and often cannot be provided in a timely manner. To address this challenge, digital breast tomosynthesis technology was utilized to generate detailed cross-sectional images of the breast tissue and integrate deep learning algorithms for image segmentation, achieving an assessment of tumor margins during surgery. this study utilized post-operative tissue samples from 46 patients who underwent breast-conserving treatment, and generated image sets using digital breast tomosynthesis for the training and evaluation of deep learning models. Deep learning algorithms effectively identifying the tumor area. They achieved a Mean Intersection over Union (MIoU) of 0.91, global accuracy of 99%, weighted IoU of 44%, precision of 98%, recall of 83%, F1 score of 89%, and dice coefficient of 93% on the training dataset; for the testing dataset, MIoU was at 83%, global accuracy at 97%, weighted IoU at 38%, precision at 87%, recall rate at 69%, F1 score at 76%, dice coefficient at 86%. The initial evaluation suggests that the deep learning-based image segmentation method is highly accurate in measuring breast tumor margins. This helps provide information related to tumor margins during surgery, and by using different datasets, this research method can also be applied to the surgical margin assessment of various types of tumors.

DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology

Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

Abstract 4171: Utilizing biopsy-free virtual histology for improved clinical efficiency in the dermatologic surgical setting

Abstract The ability to accurately define tumor margins may enhance tissue sparing and increase efficiency in the dermatologic surgery process, but no device exists that serves this role. Reflectance Confocal Microscopy (RCM) provides non-invasive cellular resolution of the skin. The only clinically-approved RCM device is bulky, non-portable, and requires a tissue cap which makes mapping of the underlying tissue impossible. We recently combined “virtual histology”, a machine learning algorithm with RCM images from this standard RCM device to generate biopsy-free histology to overcome these limitations. Whether virtual histology can be used with a portable, handheld RCM device to scan for residual tumor and tumor margins is currently unknown. We hypothesize that combining a handheld RCM device with virtual histology could provide accurate tumor margin assessment. We determined whether our established virtual histology algorithm could be applied to images from a portable RCM device and whether these pseudo-stained virtual histology images correlated with histology from skin specimens. The study was conducted as a prospective, consecutive non-randomized trial at a Veterans Affairs Medical Center dermatologic surgery clinic. All patients greater than 18 years of age with previously biopsied BCC, SCC, or SCCis were included. Successive confocal images from the epidermis to the dermis were obtained 1.5 microns apart from the handheld RCM device to detect residual skin cancer. The handheld, in-vivo RCM images were processed through a conditional generative adversarial network-based machine learning algorithm to digitally convert the images into H&amp;E pseudo-stained virtual histology images. Virtual histology of in-vivo RCM images from unbiopsied skin captured with the portable RCM device were similar to those obtained with the standard RCM device and virtual histology applied to portable RCM images correctly correlated with frozen section histology. Residual tumors detected with virtual histology generated from the portable RCM images accurately corresponded with residual tumors shown in the frozen surgical tissue specimen. Residual tumor was also not detected when excised tissue was clear of tumor following surgical procedure. Thus, the combination of virtual histology with portable RCM may provide accurate histology-quality data for evaluation of residual skin cancer prior to surgery. Combining machine learning-based virtual histology with handheld RCM images demonstrates promise in providing insights into tumor characteristics and has the potential to assist the surgeon and better guide practice decisions to more efficiently serve patients, leading to decreased layers and appointment times. Future work is needed to provide real time virtual histology, convert horizontal/confocal sections into vertical or 3D sections, and to perform clinical studies to map tumors in tissue. Citation Format: Rachel Wahhab, Nelly Kokikian, Jingxi Li, Jason Garfinkel, Stephanie Martin, David Beynet, Aydogan Ozcan, Philip Scumpia. Utilizing biopsy-free virtual histology for improved clinical efficiency in the dermatologic surgical setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4171.

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach.

Training and evaluation of the performance of a supervised deep-learning model for the segmentation of hepatic tumors from intraoperative US (iUS) images, with the purpose of improving the accuracy of tumor margin assessment during liver surgeries and the detection of lesions during colorectal surgeries. In this retrospective study, a U-Net network was trained with the nnU-Net framework in different configurations for the segmentation of CRLM from iUS. The model was trained on B-mode intraoperative hepatic US images, hand-labeled by an expert clinician. The model was tested on an independent set of similar images. The average age of the study population was 61.9 ± 9.9 years. Ground truth for the test set was provided by a radiologist, and three extra delineation sets were used for the computation of inter-observer variability. The presented model achieved a DSC of 0.84 (), which is comparable to the expert human raters scores. The model segmented hypoechoic and mixed lesions more accurately (DSC of 0.89 and 0.88, respectively) than hyper- and isoechoic ones (DSC of 0.70 and 0.60, respectively) only missing isoechoic or >20mm in diameter (8% of the tumors) lesions. The inclusion of extra margins of probable tumor tissue around the lesions in the training ground truth resulted in lower DSCs of 0.75 (). The model can accurately segment hepatic tumors from iUS images and has the potential to speed up the resection margin definition during surgeries and the detection of lesion in screenings by automating iUS assessment.

Faster intraoperative tumour margin assessment with a mobile multiphoton microscope

Faster intraoperative tumour margin assessment with a mobile multiphoton microscope

Automatic delineation of laryngeal squamous cell carcinoma during endoscopy

White Light (WL) and Narrow Band Imaging (NBI) endoscopy are widely used to assess the superficial spreading of laryngeal squamous cell carcinoma (LSCC). However, the analysis of images requires a high level of attention and extensive clinical expertise, leading to inter-clinician variability on the assessment of tumor margins. Computer-aided segmentation can automate the identification of LSCC margins, supporting clinicians in this challenging task. In this paper, we present SegMENT-Plus, a Deep Learning segmentation convolutional network specifically developed and optimized for accurate delineation of LSCC. SegMENT-Plus uses EfficienstNetB5 as encoder with a new modified Atrous Spatial Pyramid Pooling (m-ASPP) block that integrates Channel Block Attention Module (CBAM) and Squeeze Excitation (SE). In this new architecture, CBAM extracts local and global LSCC features from the encoder, while the SE block refines cancer segmentation on each dilated convolution output. SegMENT-Plus was trained and evaluated on a multi-center dataset including clinical data from three different hospitals. A total of 4289 annotated laryngeal images from 766 patients were included in this study. The experiments showed that SegMENT-Plus achieved a Dice Similarity Coefficient (DSC) between 81.4% and 84.9% and an Intersection over Union (IOU) between 81.8% and 85.7% on the data from the different hospitals, attesting its high performance and generalization capability. The proposed segmentation architecture also demonstrated statistically significant improvement in DSC and IoU compared to other state of the art architectures, showing that this work is a concrete foundation towards a clinical system for the automatic delineation of LSCC margins in endoscopic images.

Polarization-sensitive optical coherence tomography for renal tumor detection in ex vivo human kidneys

Kidney cancer is a kind of high mortality cancer because of the difficulty in early diagnosis and the high metastatic dissemination in treatments. The surgical resection of tumors is the most effective treatment for renal cancer patients. However, precise assessment of tumor margins is a challenge during surgical resection. The objective of this study is to demonstrate an optical imaging tool in precisely distinguishing kidney tumor borders and identifying tumor zones from normal tissues to assist surgeons in accurately resecting tumors from kidneys during the surgery. 30 samples from six human kidneys were imaged using polarization-sensitive optical coherence tomography (PS-OCT). Cross-sectional, enface, and spatial information of kidney samples were obtained for microenvironment reconstruction. Polarization parameters (phase retardation, optic axis direction, and degree of polarization uniformity (DOPU) and Stokes parameters (Q, U, and V) were utilized for multi-parameter analysis. To verify the detection accuracy of PS-OCT, H&E histology staining and dice-coefficient were utilized to quantify the performance of PS-OCT in identifying tumor borders and regions. In this study, tumor borders were clearly identified by PS-OCT imaging, which outperformed the conventional intensity-based OCT. With H&E histological staining as golden standard, PS-OCT precisely identified the tumor regions and tissue distributions at different locations and different depths based on polarization and Stokes parameters. Compared to the traditional attenuation coefficient quantification method, PS-OCT demonstrated enhanced contrast of tissue characteristics between normal and cancerous tissues due to the birefringence effects. Our results demonstrated that PS-OCT was promising to provide imaging guidance for the surgical resection of kidney tumors and had the potential to be used for other human kidney surgeries in clinics such as renal biopsy.

Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier.

Pediatric brain tumors are the second most common type of cancer, accounting for one in four childhood cancer types. Brain tumor resection surgery remains the most common treatment option for brain cancer. While assessing tumor margins intraoperatively, surgeons must send tissue samples for biopsy, which can be time-consuming and not always accurate or helpful. Snapshot hyperspectral imaging (sHSI) cameras can capture scenes beyond the human visual spectrum and provide real-time guidance where we aim to segment healthy brain tissues from lesions on pediatric patients undergoing brain tumor resection. With the institutional research board approval, Pro00011028, 139 red-green-blue (RGB), 279 visible, and 85 infrared sHSI data were collected from four subjects with the system integrated into an operating microscope. A random forest classifier was used for data analysis. The RGB, infrared sHSI, and visible sHSI models achieved average intersection of unions (IoUs) of 0.76, 0.59, and 0.57, respectively, while the tumor segmentation achieved a specificity of 0.996, followed by the infrared HSI and visible HSI models at 0.93 and 0.91, respectively. Despite the small dataset considering pediatric cases, our research leveraged sHSI technology and successfully segmented healthy brain tissues from lesions with a high specificity during pediatric brain tumor resection procedures.

High-resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study.

Histopathology for tumor margin assessment is time-consuming and expensive. High-resolution full-field optical coherence tomography (FF-OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF-OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF-OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 basal cell carcinomas [BCCs] and 17 squamous cell carcinomas [SCCs]). The average reader diagnostic accuracy was 88.1%, with a sensitivity of 93.7%, and a specificity of 58.3%. The artificial intelligence (AI) model achieved a diagnostic accuracy of 87.6 ± 5.9%, sensitivity of 93.2 ± 2.1%, and specificity of 81.2 ± 9.2%. A mean intersection-over-union of 60.3 ± 10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF-OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.

Development of a template for operative reporting of pediatric cancer surgery in limited-resource settings by using a modified Delphi method.

A comprehensive operative report for cancer surgery is crucial for accurate disease staging, risk stratification, and therapy escalation/de-escalation, which affects the outcome. Narrative operative reports may fail to include some critical findings. Furthermore, standardized operative reports can form the basis of a local registry, which is often lacking in limited-resource settings (LRSs). In adult literature, synoptic operative reports (SOR) contain more key findings than narrative operative reports. In the LRSs, where the capacity of diagnostic pathology services is typically suboptimal, the value of a thorough operative report is even greater. The aim of this study was to develop a SOR template to help standardize childhood cancer surgery reporting in LRSs. Twenty-three experts in pediatric cancer with extensive experience practicing in LRSs were invited to participate in a modified Delphi procedure. SOR domains for pediatric oncology surgery were drafted based on a literature search and then modified based on experts' opinions. The experts anonymously answered multiple rounds of online questionnaires until all domains and subdomains reached a consensus, which was predefined as 70% agreement. Sixteen experts participated in the study, and two rounds of the survey were completed. Twenty-one domains were considered relevant, including demographics, diagnosis, primary site, preoperative disease stage, previous tumor biopsy or surgery, preoperative tumor rupture, neoadjuvant therapy, surgical access, type of resection, completeness of resection, tumor margin assessment, locoregional tumor extension, organ resection, intraoperative tumor spillage, vascular involvement, lymph node sampling, estimated blood loss, intraoperative complications and interventions to address them, specimen names, and specimen orientation. We developed a SOR template for pediatric oncology surgery in LRSs. Consensus for all 21 domains and associated subdomains was achieved using a modified Delphi procedure.

Radioguided surgery with β decay: A feasibility study in cervical cancer

PurposeRadioguided surgery (RGS) is a technique that helps the surgeon to achieve a tumour resection as complete as possible, by means of the intraoperative detection of particles emitted by a radiotracer that bounds to tumoural cells. This study aimed to investigate the applicability of β-RGS for tumour resection and margin assessment in cervical cancer patients preoperatively injected with [18F]FDG, by means of Monte Carlo simulations. MethodsPatients were retrospectively included if they had a recurrent or persistent cervical cancer, underwent preoperative PET/CT to exclude distant metastases and received radical surgery. All PET/CT images were analysed extracting tumour SUVmax, background SUVmean and tumour-to-non-tumour ratio. These values were used to obtain the expected count rate in a realistic surgical scenario by means of a Monte Carlo simulation of the β probe, assuming the injection of 2 MBq/kg of [18F]FDG 60 min before surgery. ResultsThirty-eight patients were included. A measuring time of ∼2–3 s is expected to be sufficient for discriminating the tumour from background in a given lesion, being this the time the probe has to be over the sample in order to be able to discriminate tumour from healthy tissue with a sensitivity of ∼99% and a specificity of at least 95%. ConclusionThis study presents the first step towards a possible application of our β-RGS technique in cervical cancer. Results suggest that this approach to β-RGS could help surgeons distinguish tumour margins from surrounding healthy tissue, even in a setting of high radiotracer background activity.

Three-Dimensional Histological Electrophoresis for High-Throughput Cancer Margin Detection in Multiple Types of Tumor Specimens.

Accurate identification of tumor margins during cancer surgeries relies on a rapid detection technique that can perform high-throughput detection of multiple suspected tumor lesions at the same time. Unfortunately, the conventional histopathological analysis of frozen tissue sections, which is considered the gold standard, often demonstrates considerable variability, especially in many regions without adequate access to trained pathologists. Therefore, there is a clinical need for a multitumor-suitable complementary tool that can accurately and high-throughput assess tumor margins in every direction within the surgically resected tissue. We herein describe a high-throughput three-dimensional (3D) histological electrophoresis device that uses tumor-specific proteins to identify and contour tumor margins intraoperatively. Testing on seven cell-line xenograft models and human cervical cancer models (representing five types of tissues) demonstrated the high-throughput detection utility of this approach. We anticipate that the 3D histological electrophoresis device will improve the accuracy and efficiency of diagnosing a wide range of cancers.

Diagnostic accuracy of frozen section biopsy for early gastric cancer extent during endoscopic submucosal dissection: a prospective study

BackgroundAccurate diagnosis of the lateral extent of early gastric cancer during endoscopic submucosal dissection (ESD) is crucial to achieve negative resection margins. Similar to intraoperative consultation with a frozen section in surgery, rapid frozen section diagnosis with endoscopic forceps biopsy may be useful in assessing tumor margins during ESD. This study aimed to evaluate the diagnostic accuracy of frozen section biopsy.MethodsWe prospectively enrolled 32 patients undergoing ESD for early gastric cancer. Biopsy samples for the frozen sections were randomly collected from fresh resected ESD specimens before formalin fixation. Two different pathologists independently diagnosed 130 frozen sections as “neoplasia,” “negative for neoplasia,” or “indefinite for neoplasia,” and the frozen section diagnosis was compared with the final pathological results of the ESD specimens.ResultsAmong the 130 frozen sections, 35 were from cancerous areas, and 95 were from non-cancerous areas. The diagnostic accuracies of the frozen section biopsies by the two pathologists were 98.5 and 94.6%, respectively. Cohen’s kappa coefficient of diagnoses by the two pathologists was 0.851 (95% confidence interval: 0.837–0.864). Incorrect diagnoses resulted from freezing artifacts, a small volume of tissue, inflammation, the presence of well-differentiated adenocarcinoma with mild nuclear atypia, and/or tissue damage during ESD.ConclusionsPathological diagnosis of frozen section biopsy is reliable and can be applied as a rapid frozen section diagnosis for evaluating the lateral margins of early gastric cancer during ESD.

Dual probe difference specimen imaging for prostate cancer margin assessment.

Positive margin status due to incomplete removal of tumor tissue during radical prostatectomy for high-risk localized prostate cancer requires reoperation or adjuvant therapy, which increases morbidity and mortality. Adverse effects of prostate cancer treatments commonly include erectile dysfunction, urinary incontinence, and bowel dysfunction, making successful initial curative prostatectomy imperative. Current intraoperative tumor margin assessment is largely limited to frozen section analysis, which is a lengthy, labor-intensive process that is obtrusive to the clinical workflow within the operating room (OR). Therefore, a rapid method for prostate cancer margin assessment in the OR could improve outcomes for patients. Dual probe difference specimen imaging (DDSI), which uses paired antibody-based probes that are labeled with spectrally distinct fluorophores, was shown herein for prostate cancer margin assessment. The paired antibody-based probes consisted of a targeted probe to prostate-specific membrane antigen (PSMA) and an untargeted probe, which were used as a cocktail to stain resected murine tissue specimens including prostate tumor, adipose, muscle, and normal prostate. Ratiometric images (i.e., DDSI) of the difference between targeted and untargeted probe uptake were calculated and evaluated for accuracy using receiver operator characteristic curve analysis with area under the curve values used to evaluate the utility of the DDSI method to detect PSMA positive prostate cancer. Targeted and untargeted probe uptake was similar between the high and low PSMA expressing tumor due to nonspecific probe uptake after topical administration. The ratiometric DDSI approach showed substantial contrast difference between the PSMA positive tumors and their respective normal tissues (prostate, adipose, muscle). Furthermore, DDSI showed substantial contrast difference between the high PSMA expressing tumors and the minimally PSMA expressing tumors due to the ratiometric correction for the nonspecific uptake patterns in resected tissues. Previous work has shown that ratiometic imaging has strong predictive value for breast cancer margin status using topical administration. Translation of the ratiometric DDSI methodology herein from breast to prostate cancers demonstrates it as a robust, ratiometric technique that provides a molecularly specific imaging modality for intraoperative margin detection. Using the validated DDSI protocol on resected prostate cancers permitted rapid and accurate assessment of PSMA status as a surrogate for prostate cancer margin status. Future studies will further evaluate the utility of this technology to quantitatively characterize prostate margin status using PSMA as a biomarker.