A systematic review and meta-analysis of diagnostic performance of fluorescein-guided sentinel lymph node biopsy in early breast cancer.

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 or alternative to isotope like Indocyanine green (ICG). Both are very resource-intensive; which has prompted development of low-cost technique of Fluorescein Sodium (FS)-guided SLNB. This systematic review and meta-analysis evaluate the diagnostic performance of FS-guided SLNB in early breast cancer. OBJECTIVES: The objective was to evaluate the diagnostic performance of FS for sentinel lymph node biopsy. METHODS: Eligibility criteria: Studies where SLNB was performed using FS. 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 Sentinel Lymph Node Identification Rate (SLN-IR) and pooled False Negative Rate (FNR) using random-effects model. RESULTS: A total of 45 articles were retrieved by the initial systematic search. 7 out of the 45 studies comprising a total of 332 patients were included in the meta-analysis. The pooled SLN-IR was 93.2% (95% confidence interval [CI], 0.87-0.97; 87% to 97%). Five validation studies were included for pooling the false negative rate and included a total of 211 patients. The pooled FNR was 5.6% (95% confidence interval [CI], 2.9-9.07). CONCLUSION: Fluorescein-guided SLNB is a viable option for detection of lymph node metastases in clinically node negative patients with early breast cancer. It achieves a high pooled Sentinel Lymph Node Identification Rate (SLN-IR) of 93% with a false negative rate of 5.6% for the detection of axillary lymph node metastasis.

Different tracers for sentinel node detection in gynecologic oncology.

PURPOSE OF REVIEW: In the past decade, sentinel lymph node (SLN) mapping has progressively substituted full lymphadenectomies in gynecologic oncology. In this article, we review the most relevant and the latest literature on this topic. RECENT FINDINGS: In endometrial and cervical cancer, the current evidence further support the value of indocyanine green (ICG) as tracer of choice for SLN mapping. Experience in vulvar cancer is more limited, with ICG used together with technetium-99 m (Tc-99m) as a dual tracer but ICG, so far, has not been a game changer in this setting as it has been for cervical and endometrial cancer. SUMMARY: For most gynecologic cancers, ICG fluorescence imaging is considered now a days the tracer of choice for lymphatic mapping. However, in early-stage vulvar cancer, SLN biopsy with radioactive tracer continues to be the standard-of-care in lymph node status assessment.

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.

Malignant phyllodes tumour with lymph node metastasis: a diagnostic conundrum resolved by next generation DNA sequencing.

A malignant neoplasm with spindle cell and chondroid differentiation in the breast, metastatic to lymph node. In this context, a metaplastic carcinoma is typically favored given the exceptional nature of lymph node metastases in malignant phyllodes tumors (MPT). However, we demonstrate pathognomonic hotspot mutations in MED12 and the promoter of the TERT gene by targeted next-generation DNA sequencing, supporting a diagnosis of MPT.

Can Genomic Testing Help Refine Choosing Wisely the Omission of Axillary Staging in cN0 Breast Cancer?

INTRODUCTION: Choosing Wisely (CW) recommends women age ≥70 y with cT1-2cN0 ER+/HER2-invasive breast cancer (BC) should forgo routine axillary staging with sentinel lymph node biopsy (SLN) at the time of breast surgery. Despite this longstanding recommendation, acceptance of SLN omission has not been widely adopted. Genomic assays, such as MammaPrint (MP), may supplement the decision to apply CW. We hypothesized that having MP on BC core needle biopsy (CNB) meeting CW could provide additional information to aid in decision-making about the need for axillary staging with SLN. METHODS: A retrospective single-institution review was conducted for women with BC meeting CW criteria, who also had MP performed on CNB from 2020 to 2021. Categorical characteristics were compared using the chi-square test. Continuous variables were compared using the Mann-Whitney U-test. RESULTS: MP was available on CNB for 238 BC meeting CW criteria: 70% low risk and 30% high risk. Axillary staging was performed in 195 (82%). Eighty-one percent were pathologically node-negative and 19% were pathologically node-positive. The MP score did not correlate with pathologic nodal stage (P = 0.52). The rate of high nodal burden (pN2) was extremely low (n = 1, 0.5%). The only significant correlation with pathological node positivity was older age (P = 0.03). Appropriately, high-risk MP was strongly associated with increased recurrence risk (n = 4, P = 0.008). CONCLUSIONS: Having MP on CNB does not provide clinically meaningful information about the pN stage and does not further refine which BC patients within CW could benefit from escalation to SLN or delineate a group more likely to be pathologically node-negative.

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.

Predictive value of an ultrasound-based radiomics model for central lymph node metastasis of papillary thyroid carcinoma.

Purpose: We aimed to explore the predictive value of an ultrasound-based radiomics model for the central lymph node metastasis of papillary thyroid carcinoma. Methods: A total of 126 patients with papillary thyroid carcinoma treated between February 2021 and February 2023 were retrospectively enrolled and assigned into metastasis group (n=59, with cervical central lymph node metastasis) or non-metastasis group (n=67, without metastasis) based on surgical and pathological findings. Intergroup comparisons were conducted on the results of contrast-enhanced ultrasonography, preoperative conventional ultrasonography, as well as real-time shear wave elastography. Results: The maximum lesion diameter, echo, margin, capsule invasion, calcification, average elasticity modulus (Eavg), rising time (RT), and peak intensity (PI) had diagnostic value for papillary thyroid carcinoma, and their combination exhibited higher diagnostic value (area under the curve: 0.817). The logistic regression model was built, and the maximum lesion diameter, hypoechoic/extremely hypoechoic, lobulated or irregular margin (95% confidence interval: 1.451-6.755), capsule invasion, microcalcification/macrocalcification or peripheral calcification, high-level Eavg, low-level RT and high-level PI served as risk elements affecting papillary thyroid carcinoma from the aspect of central lymph node metastasis (odds ratio>1, P<0.05). According to the logistic regression model, the model was reliable and stable (area under the curve: 0.889, P<0.05). Conclusion: The established ultrasound-based radiomics model can be utilized for early identifying the central lymph node metastasis of papillary thyroid carcinoma.

Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis.

PURPOSE: Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets. METHODS: ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors. RESULTS: Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001). CONCLUSIONS: This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

Use of Imaging in Cutaneous Squamous Cell Carcinoma to Detect High-Risk Tumor Features, Nodal Metastasis, and Distant Metastasis: A Systematic Review.

BACKGROUND: Imaging has been shown to impact management and disease outcomes in cutaneous squamous cell carcinoma, but the literature on optimal modalities is lacking. OBJECTIVE: To perform a systematic review evaluating the performance of various imaging studies for the detection of perineural spread, bony invasion, nodal metastasis (NM), and distant metastasis in cutaneous squamous cell carcinoma. MATERIALS AND METHODS: Four databases were searched for relevant terms. Articles were included if they presented primary data on 5 or more subjects with cutaneous squamous cell carcinoma who underwent imaging to detect perineural spread, bony involvement, NM, or distant metastasis. RESULTS: Thirty studies and 1,027 subjects were included in the pooled analysis. Magnetic resonance imaging had a 94.9% sensitivity in detecting perineural spread. Computed tomography (CT) demonstrated a sensitivity of 75.7% and specificity of 98.6% in detecting bony invasion. While ultrasound, positron emission tomography-computed tomography, and CT all performed reasonably well in detecting NM, CT demonstrated the highest sensitivity (96.4%) and specificity (100%). Imaging changed management in up to 33% of cases. CONCLUSION: Imaging is useful in high-risk cutaneous squamous cell carcinoma. Magnetic resonance imaging performs best in the detection of perineural spread, and CT is the most accurate modality to detect bony invasion and NM.

Influence of Anatomic Locations of T1b Melanomas and Patient Age on Outcomes.

BACKGROUND: There are no guidelines on when to more strongly recommend sentinel lymph node biopsy (SLNB) for T1b melanomas. OBJECTIVE: To examine whether anatomic locations of T1b melanomas and patient age influence metastases. METHODS: We conducted a retrospective study using data from two hospitals in Los Angeles County from January 2010 through January 2020. RESULTS: Out of 620 patients with primary melanomas, 566 melanomas were staged based on the American Joint Committee on Cancer 8th edition melanoma staging. Forty-one were T1b, of which 13 were located on the face/ear/scalp and 28 were located elsewhere. T1b melanomas located on the face/ear/scalp had an increased risk of lymph node or distant metastasis compared with other anatomic sites (31% vs 3.6%, P=0.028). For all melanomas, the risk of lymph node or distant metastasis decreased with age of 64 years or greater (P<0.001 and P=0.034). For T1b melanomas, the risk of distant metastasis increased with increasing age (P=0.047). LIMITATIONS: Data were from a single county.  Conclusion: T1b melanomas of the face/ear/scalp demonstrated a higher risk of lymph node or distant metastasis and may help guide the recommendation of SLNB, imaging, and surveillance. Younger patients may be more strongly considered for SLNB and older patients with T1b melanomas may warrant imaging.  J Drugs Dermatol. 2024;23(5):306-310. doi:10.36849/JDD.7667.

The efficacy and assessment value of the level of thyroglobulin wash-out after fine-needle aspiration cytodiagnosis in the evaluation of lymph node metastasis in papillary thyroid carcinoma.

OBJECTIVE: The purpose of this study was to evaluate the efficacy and clinical value of US, FNAC,FNA-Tg and FNAC + FNA-Tg, as well as the cutoff values of FNA-Tg to evaluate LN metastasis. METHODS: We analyzed the diagnostic value of different US signs, the efficiency of US, FNAC, FNA-Tg and FNAC + FNA-Tg among the LN- and LN + groups, and the cutoff value of FNA-Tg to evaluate LN metastasis. We punctured LNs multiple times and measured the levels of FNA-Tg. Furthermore, the LNs were marked with immunohistochemical Tg and LCA to distinguish the presence of Tg in the para-cancerous tissue of the LNs. RESULTS: The s-Tg and FNA-Tg of the LN + group were higher than those of the LN- group (P = 0.018, ≤ 0.001). The LN + group had more abnormal US signs than the LN- group. The cutoff value of FNA-Tg was 3.2 ng/mL. US had a high sensitivity (92.42), but the specificity was not satisfactory (55.1). FNA-Tg had a higher sensitivity (92.42 vs. 89.39), specificity (100 vs. 93.88), and accuracy (92.42 vs. 83.27) than FNAC. However, the sensitivity of FNAC + FNA-Tg increased further, while the specificity and accuracy decreased slightly. The presence of Tg in the normal lymphocytes adjacent to the cancer was confirmed. CONCLUSION: Ultrasonography provides a noninvasive, dynamic, multidimensional assessment of LNs. With a cutoff value of 3.2 ng/mL, FNA-Tg has higher accuracy and a lower false-negative rate than various single diagnoses. However, FNAC combined with FNA-Tg does not cause additional pain to patients and offers a higher diagnostic efficacy and clinical value.

[The ninth edition of TNM staging for lung cancer: precise staging for precise diagnosis and treatment].

The ninth edition of TNM staging for lung cancer has been announced at the 2023 World Lung Cancer Congress and implemented from January 1, 2024. The focus of the ninth TNM staging change is dividing N2 into N2a and N2b, as well as M1c into M1c1 and M1c2. Although the T staging has not changed, it has played an important role in verifying the eighth edition of the T staging. The subdivision of stage N2 has led some patients with ⅢA of the eighth edition to experience ascending or descending stages, which will more accurately help to assess the condition and prognosis of patients with mediastinal lymph node metastasis, as well as the design of related clinical studies. Modifying the M1c staging will help define oligometastasis and explore new treatment models in the future. The ninth edition of the TNM staging system provides a more detailed division of different tumor loads, but there is no clear explanation for the staging of lung cancer after neoadjuvant therapy. Further data analysis is needed, and it is expected to be answered in the tenth edition of TNM staging.

[Fluorescence lymphography for sentinal lymph node biopsy in breast cancer]. / Sobstvennyi opyt primeneniya flyuorestsentnoi limfografii dlya biopsii signal'nykh limfouzlov pri rake molochnoi zhelezy.

OBJECTIVE: To study diagnostic value of fluorescence lymphography for sentinel lymph node biopsy in breast cancer. MATERIAL AND METHODS: The study enrolled 25 patients with breast cancer T1-2N0-1M0 between March 2023 and July 2023. Eight ones underwent neoadjuvant chemotherapy. In 3 patients, morphologically verified metastases cN1 in axillary lymph nodes regressed after treatment. After sentinel lymph node biopsy, all patients underwent standard axillary lymphadenectomy. Subareolar injection of indocyanine green 1 ml (5 mg/ml) was performed immediately before surgery. Fluorescence imaging was performed using the MARS system. RESULTS: Detection rate was 100%. Mean number of sentinel lymph nodes was 2. Metastatic lesions of sentinel lymph nodes were observed in 6 patients (24%) with micro-metastases in 2 cases. In 50% of cases, metastatic lesion did not extend beyond sentinel lymph nodes. False negative result was obtained in 1 (4%) patient. Mean number of metastases was 1.8 (max 3 in one patient). CONCLUSION: Sentinel lymph node biopsy with fluorescence lymphography is a sensitive method. The advantages of this technique are visualization of subcutaneous lymphatic vessels and skin incision for access to sentinel lymph nodes, as well as visualization of sentinel lymph nodes after skin incision.

Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study.

BACKGROUND: Accurate lymph node staging is important for the diagnosis and treatment of patients with bladder cancer. We aimed to develop a lymph node metastases diagnostic model (LNMDM) on whole slide images and to assess the clinical effect of an artificial intelligence-assisted (AI) workflow. METHODS: In this retrospective, multicentre, diagnostic study in China, we included consecutive patients with bladder cancer who had radical cystectomy and pelvic lymph node dissection, and from whom whole slide images of lymph node sections were available, for model development. We excluded patients with non-bladder cancer and concurrent surgery, or low-quality images. Patients from two hospitals (Sun Yat-sen Memorial Hospital of Sun Yat-sen University and Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China) were assigned before a cutoff date to a training set and after the date to internal validation sets for each hospital. Patients from three other hospitals (the Third Affiliated Hospital of Sun Yat-sen University, Nanfang Hospital of Southern Medical University, and the Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong, China) were included as external validation sets. A validation subset of challenging cases from the five validation sets was used to compare performance between the LNMDM and pathologists, and two other datasets (breast cancer from the CAMELYON16 dataset and prostate cancer from the Sun Yat-sen Memorial Hospital of Sun Yat-sen University) were collected for a multi-cancer test. The primary endpoint was diagnostic sensitivity in the four prespecified groups (ie, the five validation sets, a single-lymph-node test set, the multi-cancer test set, and the subset for a performance comparison between the LNMDM and pathologists). FINDINGS: Between Jan 1, 2013 and Dec 31, 2021, 1012 patients with bladder cancer had radical cystectomy and pelvic lymph node dissection and were included (8177 images and 20 954 lymph nodes). We excluded 14 patients (165 images) with concurrent non-bladder cancer and also excluded 21 low-quality images. We included 998 patients and 7991 images (881 [88%] men; 117 [12%] women; median age 64 years [IQR 56-72]; ethnicity data not available; 268 [27%] with lymph node metastases) to develop the LNMDM. The area under the curve (AUC) for accurate diagnosis of the LNMDM ranged from 0·978 (95% CI 0·960-0·996) to 0·998 (0·996-1·000) in the five validation sets. Performance comparisons between the LNMDM and pathologists showed that the diagnostic sensitivity of the model (0·983 [95% CI 0·941-0·998]) substantially exceeded that of both junior pathologists (0·906 [0·871-0·934]) and senior pathologists (0·947 [0·919-0·968]), and that AI assistance improved sensitivity for both junior (from 0·906 without AI to 0·953 with AI) and senior (from 0·947 to 0·986) pathologists. In the multi-cancer test, the LNMDM maintained an AUC of 0·943 (95% CI 0·918-0·969) in breast cancer images and 0·922 (0·884-0·960) in prostate cancer images. In 13 patients, the LNMDM detected tumour micrometastases that had been missed by pathologists who had previously classified these patients' results as negative. Receiver operating characteristic curves showed that the LNMDM would enable pathologists to exclude 80-92% of negative slides while maintaining 100% sensitivity in clinical application. INTERPRETATION: We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work. FUNDING: National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, and the Guangdong Provincial Clinical Research Centre for Urological Diseases.

A Deep Neural Network-Based Decision Support Tool for the Detection of Lymph Node Metastases in Colorectal Cancer Specimens.

The identification of lymph node metastases in colorectal cancer (CRC) specimens is crucial for the planning of postoperative treatment and can be a time-consuming task for pathologists. In this study, we developed a deep neural network (DNN) algorithm for the detection of metastatic CRC in digitized histologic sections of lymph nodes and evaluated its performance as a diagnostic support tool. First, the DNN algorithm was trained using pixel-level annotations of cancerous areas on 758 whole slide images (360 with cancerous areas). The algorithm's performance was evaluated on 74 whole slide images (43 with cancerous areas). Second, the algorithm was evaluated as a decision support tool on 288 whole slide images covering 1517 lymph node sections, randomized in 16 batches. Two senior pathologists (C.K. and C.O.) assessed each batch with and without the help of the algorithm in a 2 × 2 crossover design, with a washout period of 1 month in between. The time needed for the evaluation of each node section was recorded. The DNN algorithm achieved a median pixel-level accuracy of 0.952 on slides with cancerous areas and 0.996 on slides with benign samples. N+ disease (metastases, micrometastases, or tumor deposits) was present in 103 of the 1517 sections. The algorithm highlighted cancerous areas in 102 of these sections, with a sensitivity of 0.990. Assisted by the algorithm, the median time needed for evaluation was significantly shortened for both pathologists (median time for pathologist 1, 26 vs 14 seconds; P < .001; 95% CI, 11.0-12.0; median time for pathologist 2, 25 vs 23 seconds; P < .001; 95% CI, 2.0-4.0). Our DNN showed high accuracy for detecting metastatic CRC in digitized histologic sections of lymph nodes. This decision support tool has the potential to improve the diagnostic workflow by shortening the time needed for the evaluation of lymph nodes in CRC specimens without impairing diagnostic accuracy.

Artificial Intelligence-Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow.

Identifying lymph node (LN) metastasis in invasive breast carcinoma can be tedious and time-consuming. We investigated an artificial intelligence (AI) algorithm to detect LN metastasis by screening hematoxylin and eosin (H&E) slides in a clinical digital workflow. The study included 2 sentinel LN (SLN) cohorts (a validation cohort with 234 SLNs and a consensus cohort with 102 SLNs) and 1 nonsentinel LN cohort (258 LNs enriched with lobular carcinoma and postneoadjuvant therapy cases). All H&E slides were scanned into whole slide images in a clinical digital workflow, and whole slide images were automatically batch-analyzed using the Visiopharm Integrator System (VIS) metastasis AI algorithm. For the SLN validation cohort, the VIS metastasis AI algorithm detected all 46 metastases, including 19 macrometastases, 26 micrometastases, and 1 with isolated tumor cells with a sensitivity of 100%, specificity of 41.5%, positive predictive value of 29.5%, and negative predictive value (NPV) of 100%. The false positivity was caused by histiocytes (52.7%), crushed lymphocytes (18.2%), and others (29.1%), which were readily recognized during pathologists' reviews. For the SLN consensus cohort, 3 pathologists examined all VIS AI annotated H&E slides and cytokeratin immunohistochemistry slides with similar average concordance rates (99% for both modalities). However, the average time consumed by pathologists using VIS AI annotated slides was significantly less than using immunohistochemistry slides (0.6 vs 1.0 minutes, P = .0377). For the nonsentinel LN cohort, the AI algorithm detected all 81 metastases, including 23 from lobular carcinoma and 31 from postneoadjuvant chemotherapy cases, with a sensitivity of 100%, specificity of 78.5%, positive predictive value of 68.1%, and NPV of 100%. The VIS AI algorithm showed perfect sensitivity and NPV in detecting LN metastasis and less time consumed, suggesting its potential utility as a screening modality in routine clinical digital pathology workflow to improve efficiency.

Sentinel lymph node assessment in melanoma: current state and future directions.

Assessment of sentinel lymph node status is an important step in the evaluation of patients with melanoma for both prognosis and therapeutic management. Pathologists have an important role in this evaluation. The methodologies have varied over time, from the evaluation of dimensions of metastatic burden to determination of the location of the tumour deposits within the lymph node to precise cell counting. However, no single method of sentinel lymph node tumour burden measurement can currently be used as a sole independent predictor of prognosis. The management approach to sentinel lymph node-positive patients has also evolved over time, with a more conservative approach recently recognised for selected cases. This review gives an overview of past and current status in the field with a glimpse into future directions based on prior experiences and clinical trials.

FLI-1/Melan-A dual stain is an alternative to PRAME in differentiating metastatic melanoma from nodal nevus: A monocentric retrospective study.

Melanocytic nevi existing in lymph nodes create a diagnostic challenge by mimicking metastases. PReferentially expressed Antigen in MElanoma (PRAME) immunohistochemical (IHC) stain can differentiate one from another. FLI-1 IHC expression has been shown in malignant melanoma with variable sensitivity while melanocytic nevi were reported to be negative. We hypothesized that FLI-1/Melan-A dual IHC staining may be used in the distinction of metastatic melanoma from nodal nevi and can be an alternative and/or complementary to PRAME. In this study, we examined 13 lymph nodes with metastatic melanoma and 13 lymph nodes with benign deposits. We stained all of the lymph nodes with FLI-1, FLI-1/Melan-A dual, and PRAME IHC stains. In addition, we stained paired skin samples of the metastatic lymph nodes with FLI-1 and PRAME. In primary cutaneous melanomas, 11 of 13 were positive for FLI-1 and PRAME expression (85%). Malignant cells in 12 and 13 lymph nodes showed positive expression of PRAME and FLI-1, respectively. Only one case with a nevic cell deposit was weakly positive for FLI-1 and the remaining benign cases were negative for both FLI-1 and PRAME. Our results show that FLI-1/Melan-A dual stain is as sensitive and specific as PRAME in distinguishing lymph nodes with metastatic melanoma from nodal nevi. Further studies with larger case numbers are needed to support our significant results.

Preoperative MRI PI-RADS scores are associated with prostate cancer upstaging on surgical pathology.

INTRODUCTION: Prostate Imaging Reporting and Data System (PI-RADS) scores can help identify clinically significant prostate cancer and improve patient selection for prostate biopsies. However, the role of PI-RADS scores in patients already diagnosed with prostate cancer remains unclear. The purpose of this study was to evaluate the association of PI-RADS scores with prostate cancer upstaging. Upstaging on final pathology harbors a higher risk for biochemical recurrence with important implications for additional treatments, morbidity, and mortality. METHODS: All patients from a single high-volume institution who underwent a prostate multiparametric magnetic resonance imaging and radical prostatectomy between 2016 and 2020 were included in this retrospective analysis. Univariable and multivariable analyses were conducted to investigate potential associations with upstaging events, defined by pT3, pT4, or N1 on final pathology. A logistic regression model was constructed for the prediction of upstaging events based on PI-RADS score, prostate-specific antigen density (PSA-D), and biopsy Gleason grade groups. We built receiver operative characteristic (ROC) curves to measure the area under the curve of different predictive models. RESULTS: Two hundred and ninety-four patients were included in the final analysis. Upstaging events occurred in 137 (46.5%) of patients. On univariable analysis, patients who were upstaged on final pathology had significantly higher PI-RADS scores (odds ratio [OR] 2.34 95% confidence interval [CI] 1.64-3.40, p < 0.001) but similar PSA-D (OR 2.70 95% 0.94-8.43, p = 0.188) compared with patients who remained pT1 or pT2 on final pathology. On multivariable analysis, PI-RADS remained independently significantly associated with upstaging, suggesting it is an independent risk predictor for upstaging. Lymph node metastasis only occurred in patients with PI-RADS 4 or 5 lesions (n = 15). Our model using PSA-D, biopsy Gleason grade, and PI-RADS had a predictive AUC of 0.69 for upstaging events, an improvement from 0.59 using biopsy Gleason grade alone. CONCLUSION: PI-RADS scores are independent predictors for upstaging events and may play an important role in forecasting biochemical recurrence and lymph node metastasis. Modern nomograms should be updated to include PI-RADS to predict lymph node metastases and the likelihood of biochemical recurrence more accurately.

Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node.

BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.