Detection Of Lymph Node Metastases Research Articles

High expression of PD-L1 on conventional dendritic cells in tumour-draining lymph nodes is associated with poor prognosis in oral cancer

IntroductionOral squamous cell carcinoma (OSCC), while common and with a favorable prognosis in early stages, presents a marked reduction in survival rate upon metastasis to lymph nodes. Early detection of lymph node metastasis via biomarkers could enhance the therapeutic strategy for OSCC. Here, we explored dendritic cells (DCs) and cytotoxic T-cells in tumour-draining lymph nodes (TDLNs) as potential biomarkers.MethodDendritic cells and cytotoxic T-cells in 33 lymph nodes were analyzed with multi-parameter flow cytometry in TDLNs, regional non-TDLNs surgically excised from 12 OSCC patients, and compared to 9 lymph nodes from patients with benign conditions.ResultsOur results displayed a higher proportion of conventional cDC1s with immunosuppressive features in TDLN. Further, high PD-L1 expression on cDC1 in TDLNs was associated with metastasis and/or recurrent disease risk. Also, elevated levels of memory CD8+ T-cells and terminally exhausted PD-1+TCF-1−CD8+ T-cells were observed in TDLNs and non-TDLNs compared to healthy lymph nodes.ConclusionsWe conclude that TDLNs contain cells that could trigger an anti-tumor adaptive response, as evidenced by activated cDC1s and progenitor-like TCF-1+ T-cells. The detection of high PDL1 expression on cDC1s was indicative of TDLN metastasis and an adverse prognosis, proposing that PD-L1 on dendritic cells in TDLN could serve as a predictive biomarker of OSCC patients with a worse prognosis.

Multifrequency Magnetic Resonance Elastography Detects Small Abdominal Lymph Node Metastasis by High Stiffness

Objectives Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 is a clinical and research standard for evaluating malignant tumors and lymph node metastasis. However, quantitative analysis of nodal status is limited to measurement of short axis diameter (SAD), and metastatic lymph nodes below 10 mm in SAD are often not detected. The purpose of this study was to evaluate the value of multifrequency magnetic resonance elastography (MRE) when added to RECIST 1.1 for detection of lymph node metastasis. Materials and Methods Twenty-five benign and 82 metastatic lymph nodes were prospectively examined by multifrequency MRE at 1.5 T using tomoelastography postprocessing at 30, 40, 50, and 60 Hz (total scan time of 4 minutes). Shear wave speed as a surrogate of soft tissue stiffness was provided in m/s. Positron emission tomography–computed tomography was used as reference standard for identification of abdominal lymph node metastasis from histologically confirmed primary tumors. The diagnostic performance of MRE was compared with that of SAD according to RECIST 1.1 and evaluated by receiver operating characteristic curve analysis using generalized linear mixed models and binary logistic mixed models. Sensitivity, specificity, and predictive values were calculated for different cutoffs. Results Metastatic lymph nodes (1.90 ± 0.57 m/s) were stiffer than benign lymph nodes (0.98 ± 0.20 m/s, P < 0.001). An area under the curve of 0.95 for a cutoff of 1.32 m/s was calculated. Using a conservative approach with 1.0 specificity, we found sensitivity (SAD/MRE/MRE + SAD, 0.56/0.84/0.88), negative predictive values (0.41/0.66/0.71), and overall accuracy (0.66/0.88/0.91) to be improved using MRE and even higher for combined MRE and SAD. Conclusions Multifrequency MRE improves metastatic abdominal lymph node detection by 25% based on higher tissue stiffness—even for lymph nodes with an SAD ≤10 mm. Stiffness information is quick to obtain and would be a promising supplement to RECIST.

The Importance of the Time Interval Between Preoperative 18F-FDG PET/CT Imaging and Neck Dissection for the Detection of Nodal Metastases in Patients with Head and Neck Squamous Cell Carcinoma.

Detection of nodal metastasis is critical for the treatment and prognosis of head and neck cancer (HNC). Positron emission tomography/computed tomography (PET/CT) is increasingly being used to detect cervical lymph node involvement. The purposes of this study were to (1) investigate the diagnostic accuracy of PET/CT for the detection of neck metastasis in patients with HNC and (2) determine the effect of the time interval between surgery and PET/CT. Fifty patients with head and neck squamous cell carcinoma who underwent PET/CT before surgery were included in this study. Preoperative PET/CT images that determined lymph node metastasis were compared with the histopathological analysis of neck dissection samples. Neck dissections were divided into three groups according to the time interval between surgery and PET/CT (0-2 weeks, >2-4 weeks, and >4 weeks). The concordance between PET/CT and histopathology was measured using the neck sides at different time intervals. The specificity, sensitivity, accuracy, negative predictive value (NPV), and positive predictive value (PPV) of PET/CT in detecting metastatic lymph nodes in the neck were calculated. A total of 79 neck dissections were included in the study as 29 (58%) of the patients underwent bilateral neck dissection. The overall accuracy of PET/CT in detecting nodal metastasis was highest for the 0-2 weeks interval (95.6%). During this time interval, the sensitivity, specificity, NPV, and PPV of PET/CT were 100%, 90.9%, 100%, and 92.3%, respectively. Although PET/CT is an important and reliable diagnostic method for detecting nodal metastases in patients with HNC, its reliability decreases as the time between surgeries increases. The optimal interval was 2 weeks; however, up to 4 weeks was acceptable.

Stepwise Ultra-pH-Sensitive Micelles Overcome a pKa Barrier for Systemic Lymph Node Delivery.

While local nanoparticle delivery to lymph nodes is well studied, there are few design criteria for intravenous delivery to the entire lymph node repertoire. In this study, we investigated the effect of NP pH transition on lymph node targeting by employing a series of ultra-pH-sensitive (UPS) polymeric micelles. The UPS library responds to pH thresholds (pKa 6.9, 6.2, and 5.3) over a range of physiological pH. We observed a dependence of intravenous lymph node targeting on micelle pH transition. UPS6.9 (subscript indicates pKa) shows poor lymph node delivery, while UPS5.3 delivers efficiently to lymph node sets. We investigated targeting mechanisms of UPS5.3, observing an accumulation among lymph node lymphatics and a dependence on lymph node-resident macrophages. To overcome the pH-threshold barrier, which limits UPS6.9, we rationally designed a nanoparticle coassembly of UPS6.9 with UPS5.3, called HyUPS. The HyUPS micelle retains the constitutive pH transitions of each polymer, showing stepwise responses to discrete pH thresholds. We demonstrate that HyUPS improves UPS6.9 delivery to lymph nodes, extending this platform for disease detection of lymph node metastasis.

Clinicopathological parameters associated with cervical lymph node metastases in differentiated thyroid cancer.

Lymph node metastasis (LNM) has an important role for the prognosis of diferentiated thyroid cancer (DTC). The aim of the study was to investigate the efect of clinicopathologic parameters on cervical LNM in DTC. The patients who underwent thyroidectomy along with neck dissection were analyzed retrospectively. Of the 150 patients diagnosed with DTC who underwent neck dissection, 1 had follicular thyroid carcinoma and 149 had papillary thyroid carcinoma (PTC). The median tumor size was 14.0 mm. The tumor diameter with the highest specificity and sensitivity for the detection of LNM was >11.5 mm. Extrathyroidal extension (ETE) was observed in 35.3% of the patients. The rate of multifocality in tumors with extrathyroidal spread was significantly higher than in tumors without ETE. LNM was observed in 60.0% of the patients. ETE was present in 28.9% of the tumors that had LNM. Lymphovascular invasion (LVI), perineural invasion (PNI), and positive surgical margin were observed in 13.3%, 2.7%, and 14% of the patients respectively. A significant positive correlation was found between LNM and tumor diameter, ETE, positive tumor margin and LVI (Pp=.006, P=.031, P = .002, and P=.014, respectively). In this study, ETE, LVI, positive tumor margin, and tumor diameter greater than 11.5 mm were significantly correlated with the presence of LNM. These findings may be useful in bringing to mind the possibility of lymph node metastases that have not been able to be detected before surgery and in monitoring these patients more closely.

[18F]FAPI adds value to [18F]FDG PET/CT for diagnosing lymph node metastases in stage I-IIIA non-small cell lung cancer: a prospective study

BackgroundThis study investigates the value of fluorine 18 ([18F])-labeled fibroblast activation protein inhibitor (FAPI) for lymph node (LN) metastases in patients with stage I-IIIA non-small cell lung cancer (NSCLC).MethodsFrom November 2021 to October 2022, 53 patients with stage I-IIIA NSCLC who underwent radical resection were prospectively included. [18F]-fluorodeoxyglucose (FDG) and [18F]FAPI examinations were performed within one week. LN staging was validated using surgical and pathological findings. [18F]FDG and [18F]FAPI uptake was compared using the Wilcoxon signed-ranks test. Furthermore, the diagnostic value of nodal groups was investigated.ResultsIn 53 patients (median age, 64 years, range: 31–76 years), the specificity of [18F]FAPI for detecting LN metastasis was significantly higher than that of [18F]FDG (P < 0.001). High LN risk category, greater LN short-axis dimension(≥ 1.0 cm), absence of LN calcification or high-attenuation, and higher LN FDG SUVmax (≥ 10.1) were risk factors for LN metastasis(P < 0.05). The concurrence of these four risk factors accurately predicted LN metastases (Positive Predictive Value [PPV] 100%), whereas the presence of one to three risk factors was unable to accurately discriminate the nature of LNs (PPV 21.7%). Adding [18F]FAPI in this circumstance improved the diagnostic value. LNs with an [18F]FAPI SUVmax<6.2 were diagnosed as benign (Negative Predictive Value 93.8%), and LNs with an [18F]FAPI SUVmax≥6.2 without calcification or high-attenuation were diagnosed as LN metastasis (PPV 87.5%). Ultimately, the integration of [18F]FDG and [18F]FAPI PET/CT resulted in the highest accuracy for N stage (83.0%) and clinical decision revisions for 29 patients.ConclusionIn patients with stage I-IIIA NSCLC, [18F]FAPI contributed additional valuable information to reduce LN diagnostic uncertainties after [18F]FDG PET/CT. Integrating [18F]FDG and [18F]FAPI PET/CT resulted in more precise clinical decisions.Trial registrationThe Chinese Clinical Trial Registry: ChiCTR2100044944 (Registered: 1 April 2021, https://www.chictr.org.cn/showprojEN.html?proj=123995).

Combining machine and deep transfer learning for mediastinal lymph node evaluation in patients with lung cancer

BackgroundThe prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis. Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis, thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis MethodsIn total, 623 eligible patients were recruited from two medical institutions. Seven deep learning models, namely Alex, GoogLeNet, Resnet18, Resnet101, Vgg16, Vgg19, and MobileNetv3 (small), were utilized to extract deep image histological features. The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient (r ≥ 0.9) and Least Absolute Shrinkage and Selection Operator. Eleven machine learning methods, namely Support Vector Machine, K-nearest neighbor, Random Forest, Extra Trees, XGBoost, LightGBM, Naive Bayes, AdaBoost, Gradient Boosting Decision Tree, Linear Regression, and Multilayer Perceptron, were employed to construct classification prediction models for the filtered final features. The diagnostic performances of the models were assessed using various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Calibration and decision-curve analyses were also performed. ResultsThe present study demonstrated that using deep radiomic features extracted from Vgg16, in conjunction with a prediction model constructed via a linear regression algorithm, effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer. The performance of the model was evaluated based on various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, which yielded values of 0.808, 0.834, 0.851, 0.745, 0.829, and 0.776, respectively. The validation set of the model was assessed using clinical decision curves, calibration curves, and confusion matrices, which collectively demonstrated the model's stability and accuracy ConclusionIn this study, information on the deep radiomics of Vgg16 was obtained from computed tomography images, and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.

Papillary thyroid carcinoma: ≤ 10mm does not always mean pN0. A multicentric real-world study.

The incidence of papillary thyroid carcinoma (PTC) is increasing and PTC ≤ 10mm (PTMC) accounts for most new diagnoses. PTMCs are not always low risk, as detection of lymph nodes metastasis (LNM) may occur. The purpose of the study was to analyze the clinical pattern, frequency, and independent risk factors of patients with PTMC and LNM. From January 2022 to June 2023, PTCs managed at CTO Hospital, Rome; Policlinico Vanvitelli, Naples; and Garibaldi Nesima Hospital, Catania were included. PTC management followed the same diagnostic-therapeutic procedures according to the ATA guidelines. Variables such as age, sex, maximum diameter, histologic evidence of LNM (HELNM +), Hashimoto's thyroiditis (HT), multifocality, capsule invasion, and histological subtype were considered. PTCs were divided according to HELNM and size. Two hundred ninety-eight PTCs were included. PTMCs were 136 (45.6%) and LNM occurred in 27.2% of them. In the HELNM + group, analysis of PTMC vs 'MacroPTC' (PTC > 10mm) did not show any statistical difference. Multivariate regression revealed that young age (OR 0.93; CI 95% 0.90-0.96; p < 0.01) and male sex (male OR 3.44; CI 95% 1.16-10.20; p = 0.03) were the only independent risk factors for HELNM + in PTMC. The risk of LNM in PTMC is not negligible; therefore, a careful evaluation by an expert thyroidologist is mandatory for patients with small thyroid nodule, especially in younger and male patients before excluding surgery. In the future, new tools are needed to detect early PTMC with LNM before surgery.

The Diagnostic Impact of Contrast-Enhanced Computed Tomography (CECT) in Evaluating Lymph Node Involvement in Colorectal Cancer: A Comprehensive Review.

Colorectal cancer (CRC) remains a significant global health burden, necessitating accurate staging and treatment planning for optimal patient outcomes. Lymph node involvement is a critical determinant of prognosis in CRC, emphasizing the importance of reliable imaging techniques for its evaluation. Contrast-enhanced computed tomography (CECT) has emerged as a cornerstone in CRC imaging, offering high-resolution anatomical detail and vascular assessment. This comprehensive review synthesizes the existing literature to evaluate the diagnostic impact of CECT in assessing lymph node involvement in CRC. Key findings highlight CECT's high sensitivity and specificity in detecting lymph node metastases, facilitating accurate staging and treatment selection. However, challenges such as limited resolution for small lymph nodes and potential false-positives call for a cautious interpretation. Recommendations for clinical practice suggest the integration of CECT into multidisciplinary treatment algorithms, optimizing imaging protocols and enhancing collaboration between radiologists and clinicians. Future research directions include refining imaging protocols, comparative effectiveness studies with emerging modalities, and prospective validation of CECT's prognostic value. Overall, this review stresses the pivotal role of CECT in CRC management and identifies avenues for further advancements in imaging-guided oncology care.

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases.

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

Comparison of diagnostic value of quantitative parameters from FAPI and FDG PET/CT in the detection of mediastinal lymph node metastasis in non-small cell lung cancer

Comparison of diagnostic value of quantitative parameters from FAPI and FDG PET/CT in the detection of mediastinal lymph node metastasis in non-small cell lung cancer

68Ga]Ga-FAPI-46 PET/CT for penile cancer - a feasibility study.

Penile cancer is a rare entity and has a good prognosis in localized stage. Delayed surgical treatment of lymphatic disease is associated with poor overall survival but conventional imaging cannot detect occult lymph node metastasis sufficiently. Imaging cancer related fibroblasts has shown promising results as non-invasive staging tool in various tumor entities but has not yet been evaluated in penile cancer. In this single-center pilot study, patients planned for surgical treatment for penile cancer underwent preoperatively [68Ga]Ga-FAPI-46 PET/CT. Post-operative histopathology was compared to [68Ga]Ga-FAPI-46 PET/CT results. From January 2022 to June 2022, a total number 11 patients with histopathologically proven penile cancer underwent surgery and received [68Ga]Ga-FAPI-46 PET/CT prior therapy. 8 primary tumor sites and 4 lymph node regions were analyzed. FAPI uptake was increased on primary tumor site (SUVmax 16.2 (9.1 - 25.8)). Histopathological proven lymph node regions showed highly increased FAPI uptakes (SUVmax 17.9 (16.4 - 23.5) on [68Ga]Ga-FAPI-46 PET/CT. In this first pilot cohort, there were no false-positive FAPI uptake which might allow the detection of occult lymph node metastasis by [68Ga]Ga-FAPI-46 PET/CT and might consequently lead to omitting lymph node regions during surgery that had no increased FAPI uptake pre-operatively.

Abstract IA022: AI in bladder cancer pathology and research: What can we expect and what not?

Abstract Artificial intelligence (AI) applications are rapidly evolving, disruptive technologies that recently attracted tremendous public awareness including euphoria, bull runs at stock exchanges but also criticism and skepticism. Pathology, a crucial clinical diagnostic discipline, faces significant challenges despite its critical role and broad scope. A notable issue is the declining interest among medical graduates in pursuing further training in Surgical Pathology and Molecular Pathology. This disinterest is compounded by a trend towards part-time work and the aging demographic in Western industrial nations, which increases the demand for precise cancer diagnoses, molecular pathology and advanced oncological treatments. As the need for skilled pathologists grows, the availability of these professionals is steadily decreasing. Furthermore, political policies in heavily regulated healthcare systems (e.g., Germany) are exacerbating the shortage by limiting residency and specialist training positions despite the growing need. Adding to the reluctance among medical graduates to consider pathology as discipline is the looming “threat”” of replacing the field with Artificial Intelligence (AI) technologies. However, historical precedents suggest that necessity often spurs significant innovation. AI's role in pathology is expanding, reshaping the field with new technological advancements. AI, broadly encompassing various computational algorithms designed to perform tasks typically requiring human intelligence such as learning, reasoning, and pattern recognition, is now being applied extensively across different domains including medical diagnostics. Recent implementations in pathology include AI-driven algorithms that automate processes such as the Gleason grading of prostate biopsies, lymph node metastasis detection, detection of molecular bladder cancer subtypes or grading of kidney cancer showing high accuracy levels, speed and reproducibility in delivering results. These tools, while they do not replace pathologists, significantly reduce their workload by automating routine tasks, thus freeing up time for more complex diagnostic activities or simply dealing with the increasing workload. Despite the exciting possibilities, the use of AI in predicting molecular alterations, such as FGFR3 mutations in bladder cancer, faces limitations due to insufficient sensitivity and specificity, indicating that such technologies are not yet suitable for screening purposes. In conclusion, AI is increasingly vital in addressing the challenges of modern pathology, particularly in compensating for the shortage of pathologists. As technology advances, its integration into clinical practice offers promising solutions to enhance diagnostic accuracy and efficiency in uropathology, paving the way for more personalized and effective patient care. Citation Format: Markus Eckstein. AI in bladder cancer pathology and research: What can we expect and what not? [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr IA022.

Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via Artificial Intelligence-Based Pathology

Introduction: Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer’s stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent. Methods: Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers. Results: It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925. Conclusion: Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.

Abstract PS05-03: MagSense® HER2, a Molecularly Targeted Magnetic Resonance Imaging Agent for the Detection of Axillary Nodal Metastasis in Subjects with Human Epidermal Growth Factor Receptor 2 Positive (HER2+) Breast Cancer

Abstract Background Precise nodal staging is critical in guiding systemic and regional treatments including surgery in the management of breast cancer. Regional nodal assessment includes axillary imaging (mostly by ultrasound) and lymph node sampling. Since imaging findings suggestive of nodal metastasis are based on size and morphologic changes and not specific to tumor type, pathologic confirmation through either biopsy or removal of sentinel lymph node (SLN) remains the gold standard. Imagion is developing the MagSense® HER2 Imaging Agent (MSH2IA), an anti-human epidermal growth factor receptor 2 (HER2) conjugated with iron oxide nanoparticles, for the detection of nodal metastasis in HER2+ breast cancer patients. MSH2IA is currently being investigated in a first-in-human phase 1 study (ACTRN12621000126819) in HER2+ breast cancer patients who are diagnosed to have suspicious nodes by conventional imaging. Methods The key objectives of the phase 1 study are safety and tolerability of MSH2IA and confirmation that MSH2IA drains to the axillary lymph nodes. The study also explores MSH2IA’s ability to detect metastatic nodes when used with magnetic resonance imaging (MRI), with tumor status assessed by standard tissue histopathology. All eligible subjects receive a 22.5 mg (iron equivalent) subareolar or peritumoral injection of MSH2IA. MRI of the axilla are obtained before and 24 to 72 hours after MSH2IA injection. Core biopsy or dissection of a node suspicious by ultrasound is obtained for histopathology assessments. After completing the first cohort of 6 subjects, the protocol was amended to include the insertion of an MRI compatible clip in a node suspicious by ultrasound to localize the suspicious node for core biopsy and postdose MRI scan. Review of MRI scans and histopathology are performed in respective central laboratories. Results Thirteen participants with HER2+ breast cancer have completed the study. MSH2IA appears safe and well tolerated. The imaging agent, as administered, is detectable by MRI and pathology, confirming that MSH2IA drains to the lymph nodes via the chosen route of administration. In postdose MRI scans, study central radiologists reported distinct and differentiable MRI appearance in morphologically normal and suspicious nodes. The presence of a distinct postdose MR image appearance was confirmed by a group of independent radiologists in a separate blinded review. While normal nodes appeared with homogeneous hypointensity, morphologically suspicious nodes showed partial irregular darkening and/or speckled heterogeneous hypointensity. In evaluable subjects, histopathology of the core biopsies obtained from the clipped nodes confirmed the presence of HER2+ nodal metastasis, providing preliminary evidence for node-level concordance between MRI vs pathology observations. Safety, imaging, and pathology results from all available subjects will be presented. This phase 1 study in Australia will be closing enrollment by July 31, 2023. A phase 2 study in the United States and elsewhere is being planned with the objectives of optimizing dose, schedule, and imaging parameters for best diagnostic performance for clinical utility. Conclusion Available data from the ongoing phase 1 study show that MSH2IA appears safe and drains to the axillary lymph nodes. MSH2IA displays a distinct and differentiable MRI appearance in nodes morphologically highly suspicious for tumor, which is confirmed to be HER2+ metastasis by histopathology by node level concordance. This data provides preliminary proof of principle for the mechanism of molecularly targeted MR imaging and suggests that MSH2IA has the potential to provide tumor-specific MR imaging not currently available in conventional axillary imaging. Citation Format: Jane Fox, Shanta Velaiutham, Natalie Yang, Eddie lau, Kowsi Murugappan, Beena Kumar, Alex Laslowski, Koushik Govindarajan, Steven Reich, Stephen Thomas, Nitika Neha, Marie Zhang, Isaac Bright. MagSense® HER2, a Molecularly Targeted Magnetic Resonance Imaging Agent for the Detection of Axillary Nodal Metastasis in Subjects with Human Epidermal Growth Factor Receptor 2 Positive (HER2+) Breast Cancer [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 PS05-03.

Abstract PO4-27-10: A Systematic Review and Meta-analysis of Diagnostic Performance of Fluorescein Guided Sentinel Lymph Node Biopsy in Early Breast Cancer

Abstract Background: Evaluation of axillary lymph nodes status in cN0 axilla is performed by sentinel lymph node biopsy (SLNB) utilizing a combination of radioactive isotope and blue dye. There is a paucity of radioactive tracer in many countries of transitional economies. Hence, alternatives to isotope have been explored, viz. indocyanine green (ICG), superparamagnetic iron oxide nanoparticles and Fluorescein Sodium (FS). This systematic review and meta-analysis, evaluates the diagnostic performance of Fluorescein Sodium guided SLNB. Objectives: The objective was to evaluate the diagnostic performance of Fluorescein Sodium for sentinel lymph node biopsy in early breast cancer. Methods: PRISMA 2020 guidelines were followed for this meta-analysis. Eligibility criteria: Studies where SLNB was performed using Fluorescein Sodium for breast carcinoma. Information sources: PubMed, EMBASE, Cochrane library and online clinical trial registers Risk of bias: Articles were assessed for risk of bias using the QUADAS-2 tool. Synthesis of results: The main summary measures were pooled detection rate and pooled false negative rate using random effects model. In order to describe heterogeneity, we constructed summary receiver operating characteristic (SROC) curves with 95% prediction regions, estimated using bivariate meta-analysis with a test level random effect only, and forest plots. Calculations were performed using the Meta-Disc v1.4 program (available from https://metadisc.software.informer.com/1.4/). Results: Out of 45 studies found on initial systematic search, 7 studies were found eligible and were included in the meta-analysis based on data on 332 patients. The pooled detection rate was 93.2% (95% confidence interval [CI] 87% to 97%).). The heterogeneity (I2 value) of the pooled detection rate was 71%. Five validation studies were included for pooling the false negative rate (FNR) and included a total of 211 patients. The pooled FNR was 5.6% (95% CI; 2.9 to 9.07). Five validation studies were included for sensitivity, specificity, diagnostic odds ratio, LR+ and LR- meta-analysis. With regards to the identification of SLN the pooled sensitivity was 80% [(95% CI, 68% to 89%); I2=79.8]; specificity was 98% [(95% CI, 95% to 100%); I2=0.0]. The likelihood ratio for a positive test LR+ was 23.2 [(95% CI, 5.91 to 95.18); I2=0.0] and likelihood ratio for a negative test a LR- of 0.41 [(95% CI, 0.16 to 1.05); I2=88.1]. The diagnostic Odds ratio OR was 76.94 (95% CI, 15.88 to 372.79). Diagnostic accuracy as measured by SROC was 99.08%. Q index was 0.96. Publication bias for pooled detection rate as per Egger’s test was -2.2 (p= 0.66) and for FNR was 1.85 (p=0.29). Visual inspection of summary ROC curve and of forest plots did not show significant heterogeneity. Conclusion: Fluorescein guided SLNB is an affordable option for detection of lymph node metastases in cN0 patients with early breast cancer. It achieves a high detection rate of 93% with a false negative rate of 5.6% for the detection of axillary lymph node metastasis. We recommend larger multinational trials to confirm its high detection rate with low false negativity. Citation Format: Sanjay Kumar Yadav, Bharath S, Sarada Khadka, Dhananjaya Sharma, Chandan Kumar Jha, Gaurav Agarwal, Manju Singh, Anurag Srivastava, Amit Goyal. A Systematic Review and Meta-analysis of Diagnostic Performance of Fluorescein Guided Sentinel Lymph Node Biopsy in Early Breast Cancer [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 PO4-27-10.

Preoperative assessment of lymph nodal metastases with [68Ga]Ga-DOTATOC PET radiomics for improved surgical planning in well-differentiated pancreatic neuroendocrine tumours.

Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [68Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention. Seventy-two patients who performed preoperative [68Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [68Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models' bACC, SN, SP, PPV and NPV were collected and compared (Kruskal-Wallis, Mann-Whitney). LN metastases were detected in 29/72 patients at histology. [68Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p < 0.05*). In this study, [68Ga]Ga-DOTATOC PET radiomics allowed to increase diagnostic sensitivity in detecting LN metastases from 24 to 77% in NF-PanNET patients candidate to surgery. Especially in case of micrometastatic involvement, this approach might assist clinicians in a better patients' stratification.

Incidence of sentinel lymph node metastases in apparent early-stage endometrial cancer: a multicenter observational study

ObjectiveUltrastaging is accurate in detecting nodal metastases, but increases costs and may not be necessary in certain low-risk subgroups. In this study we examined the risk of nodal involvement detected...

Fast-Track Development and Multi-Institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer

Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework.The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems.A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels).A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

Head-to-head comparison of the diagnostic performance between 68Ga-FAPI-04 PET/CT and 18F-FDG PET/CT in colorectal cancer: a systematic review and meta-analysis.

This study aimed to compare the detection rates of 68Ga-FAPI-04 PET/CT and 18F-FDG PET/CT in colorectal cancer. A systematic search of major medical databases was conducted to identify studies up to September 2023. The primary outcome assessed was the detection rate of 68Ga-FAPI-04 PET/CT and 18F-FDG PET/CT in the primary tumor. Pooled risk ratios with a 95% CI were calculated using random-effect models to adjust for heterogeneity. Eight studies were included in the meta-analysis. 68Ga-FAPI-04 PET/CT has higher uptakes in lymph nodes, bone, and peritoneal metastasis compared with 18F-FDG PET/CT. The detection rate of 68Ga-FAPI-04 PET/CT based on lesion was better for lymph node metastasis (RR = 0.63, 95% CI 0.47-0.84, P = 0.002) and peritoneal metastasis (RR = 0.52, 95% CI 0.32-0.85, P = 0.009), both imaging modalities had essentially the same diagnostic efficacy in primary tumor (RR = 0.99, 95% CI 0.96-1.02, P = 0.49). 68Ga-FAPI-04 as a highly promising PET/CT tracer was superior to 18F-FDG PET/CT in colorectal cancer, especially in detecting lymph node metastases and peritoneal metastases.