Non-sentinel Node Research Articles

A new prediction nomogram of non-sentinel lymph node metastasis in cT1-2 breast cancer patients with positive sentinel lymph nodes

We aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training (n = 410), internal validation (n = 298), and external validation cohorts (n = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) (P < 0.001), the proportion of positive SLNs (P = 0.029), lymph-vascular invasion (P = 0.029), perineural invasion (P = 0.023), and estrogen receptor (ER) status (P = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676–0.785) for the training, 0.701 (95% CI 0.630–0.773) for internal validation, and 0.813 (95% CI 0.734–0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.

Correlation between sentinel lymph node biopsy and non-sentinel lymph node metastasis in patients with cN0 breast carcinoma: comparison of invasive ductal carcinoma and invasive lobular carcinoma

BackgroundSome studies have suggested that axillary lymph node dissection (ALND) can be avoided in women with cN0 breast cancer with 1–2 positive sentinel nodes (SLNs). However, these studies included only a few patients with invasive lobular carcinoma (ILC), so the validity of omitting ALDN in these patients remains controversial. This study compared the frequency of non-sentinel lymph nodes (non-SLNs) metastases in ILC and invasive ductal carcinoma (IDC). Materials Methods: Data relating to a total of 2583 patients with infiltrating breast carcinoma operated at our institution between 2012 and 2023 were retrospectively analyzed: 2242 (86.8%) with IDC and 341 (13.2%) with ILC. We compared the incidence of metastasis to SLNs and non-SLNs between the ILC and IDC cohorts and examined factors that influenced non-SLNs metastasis. Results: SLN biopsies were performed in 315 patients with ILC and 2018 patients with IDC. Metastases to the SLNs were found in 78/315 (24.8%) patients with ILC and in 460 (22.8%) patients with IDC (p = 0.31). The incidence of metastases to non-SLNs was significantly higher (p = 0.02) in ILC (52/78–66.7%) compared to IDC (207/460 − 45%). Multivariate analysis showed that ILC was the most influential predictive factor in predicting the presence of metastasis to non-SLNs. Conclusions: ILC cases have more non-SLNs metastases than IDC cases in SLN-positive patients. The ILC is essential for predicting non-SLN positivity in macro-metastases in the SLN. The option of omitting ALND in patients with ILC with 1–2 positive SLNs still requires further investigation.

Investigation of prognostic factors for non-sentinel lymph node metastasis in patients undergoing sentinel lymph node biopsy for cutaneous malignant melanoma: Experience from a reference centre in Turkey

BackgroundIn the light of the results of recent randomised controlled trials regarding the role of nodal observation and completion lymph node dissection (CLND), studies from different populations are needed. The aim of our study was to present our experience with sentinel lymph node biopsy (SLNB) and CLND and the clinical and histopathological factors associated with a positive non-sentinel node. MethodsIn this single-centre, retrospective study, we reviewed histopathological reports of patients with primary cutaneous melanoma who underwent SLNB and CLND over a period of 7 years. The primary outcomes were the positivity rates of SLNBs and CLNDs. Secondary outcomes were metastatic tumour burden in positive sentinel nodes and presence of perinodal invasion. ResultsAmong the 110 participants who underwent SLNB (53 females, 57 males), the mean Breslow thickness of the primary tumour was 4.1 (0.3–41) mm. Ulceration appeared in 62.7% of lesions. The SLNBs were positive in 38 patients (34.5%), with 35 (92.1%) undergoing CLND, among which 9 (25.7%) showed metaNBstasis. Positive SLNB was linked to a higher Breslow thickness (p = 0.022), whereas CLND results lacked such an association (p = 0.76). Perinodal invasion (p = 0.006) and sentinel lymph node metastasis exceeding 1 mm (p = 0.017) was associated with a higher probability of non-sentinel node metastasis. ConclusionTo adapt the results of the new cohort study on SLNB and melanoma to different populations, studies with different patient groups highlighting the problems and suggested solutions are needed.

Association between postoperative pathological results and non-sentinel nodal metastasis in breast cancer patients with sentinel lymph node-positive breast cancer

ObjectiveFor patients with 1–2 positive sentinel lymph nodes (SLN) identified by biopsy, the necessity of axillary lymph node dissection (ALND) remains a matter of debate. The primary aim of this study was to investigate the association between postoperative pathological factors and non-sentinel lymph node (NSLN) metastases in Chinese patients diagnosed with sentinel node-positive breast cancer.MethodsThis research involved a total of 280 individuals with SLN-positive breast cancer. The relationship between postoperative pathological variables and non-sentinel lymph node metastases was scrutinized using univariate, multivariate, and stratified analysis.ResultsAmong the 280 patients with a complete count of SLN positives, 126 (45.0%) exhibited NSLN metastasis. Within this group, 45 cases (35.71%) had 1 SLN positive, while 81 cases (64.29%) demonstrated more than 1 SLN positive. Multivariate logistic regression analysis revealed that HER2 expression status (OR 2.25, 95% CI 1.10–4.60, P = 0.0269), LVI (OR 6.08, 95% CI 3.31–11.14, P < 0.0001), and the number of positive SLNs (OR 4.17, 95% CI 2.35–7.42, P < 0.0001) were positively correlated with NSLNM.ConclusionIn our investigation, the risk variables for NSLN metastasis included LVI, HER2 expression, and the quantity of positive sentinel lymph nodes. However, further validation is imperative, including this institution, distinct institutions, and diverse patient populations.

Assessing the sentimag system for guiding sentinel node biopsies in patients with breast cancer

ABSTRACT Introduction Sentinel lymph node biopsy for breast cancer is a method to localize and excise the first draining lymph node from an invasive cancer of the breast. The histopathologic evaluation of the sentinel lymph node is used for predicting recurrence and survival and thus, guiding oncologists for treatment-decision making to administer adjuvant therapies. The ability to identify the sentinel node depends on methods to map lymphatic drainage from the breast to the sentinel node and accurately discriminate that node from other non-sentinel lymph nodes of the axilla. Areas Covered This review covers the clinical demand for technologies to assist the surgeon in intraoperative lymphatic mapping to specifically identify the sentinel lymph node in patients with breast cancer. Performance characteristics are reviewed for superparamagnetic iron oxide tracers used in lymphatic mapping compared to other current available technologies for lymphatic mapping. Expert opinion The Magtrace (superparamagnetic iron oxide tracer) Sentimag (handheld magnetic probe) system is an FDA-approved technology for intraoperative lymphatic mapping to facilitate sentinel lymph node biopsy in breast cancer with technologic performance characteristics that are equivalent to 99Technetium-sulfur colloid. Barriers to broader utilization primarily center around the need for nonmetallic devices to be used for the conduct of surgery which would interfere with the paramagnetic method for tracer localization.

Distribution pattern and pathologic analysis of metastatic sentinel and non-sentinel lymph nodes in lymphatic basin dissection for clinical T2/T3 oral cancer with clinical N0 status

Distribution pattern and pathologic analysis of metastatic sentinel and non-sentinel lymph nodes in lymphatic basin dissection for clinical T2/T3 oral cancer with clinical N0 status

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer.

It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection. To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer. This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model. Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

Non-sentinel lymph node metastases risk factors in patients with breast cancer with one or two sentinel lymph node macro-metastases

Approximately 59 % of patients with breast cancer with one or two sentinel lymph nodes (1–2 SLN) macrometastases do not benefit from axillary lymph node dissection (ALND), which may also incur morbidities. It is necessary to evaluate the association between various clinicopathological characteristics and non-sentinel lymph node metastases (non-SLNM) in patients with breast cancer with 1–2 SLN macrometastases, and determine whether they 1–2 should avoid ALND. Eight electronic literature databases (PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journal, Wanfang, and Chinese Biomedical Literature) were searched from their inception to June 30, 2023, and two reviewers independently extracted the data and assessed the risk of bias. Association strength was summarized using odds ratios (OR) and 95 % confidence intervals (CI). Heterogeneity was accounted for using a subgroup analysis. Publication bias was evaluated using funnel plots and Egger's test. There were 25 studies with 8021 participants, and 27 potential risk factors were evaluated. The risk factors for non-SLNM in patients with 1–2 SLN macrometastatic breast cancer include the following: factors of primary tumor: multifocality (OR (95 % CI (2.63 (1.96, 3.54))), tumor size ≥ T2 (2.64 (2.22, 3.14)), tumor localization (upper outer quad) (2.06 (1.23, 3.43)), histopathological grade (G3) (2.45 (1.70, 3.52)), vascular invasion (VI) (2.60 (1.35, 4.98)), lymphovascular invasion (LVI) (2.87 (1.80, 4.56)), perineural invasion (PNI) (3.16 (1.18,8.43)). Factors of lymph nodes: method of SLNs detected (blue dye) (3.85 (1.54, 9.60)), SLN metastasis ratio ≥0.5 (2.79 (2.24, 3.48)), two positive SLNs (3.55, (2.08, 6.07)), zero negative SLN (3.72 (CI 2.50, 4.29)), extranodal extension (ENE) (4.69 (2.16, 10.18)). Molecular typing: Her-2 positive (2.08 (1.26, 3.43)), Her-2 over-expressing subtype (1.83 (1.22, 2.73)). Factors of examination/inspection: axillary lymph nodes (ALNs) positive on imaging (3.18 (1.68, 6.00)), cancer antigen 15-3 (CA15-3) (4.01 (2.33,6.89)), carcinoembryonic antigen (CEA) (2.13 (1.32–3.43)). This review identified the risk factors for non-SLNM in patients with 1–2 SLN macrometastatic breast cancer. However, additional studies are needed to confirm the above findings owing to the limited number and types of studies included.

Predictors of Non-Sentinel Lymph Node Metastasis in Patients with Positive Sentinel Lymph Node in Early-Stage Cervical Cancer: A SENTICOL GROUP Study.

The goal of this study was to identify the risk factors for metastasis in the remaining non-sentinel lymph nodes (SLN) in the case of positive SLN in early-stage cervical cancer. An ancillary analysis of two prospective multicentric databases on SLN biopsy for cervical cancer (SENTICOL I and II) was performed. Patients with early-stage cervical cancer (FIGO 2018 IA to IIA1), with bilateral SLN detection and at least one positive SLN after ultrastaging, were included. 405 patients were included in SENTICOL I and Il. Fifty-two patients had bilateral SLN detection and were found to have SLN metastasis. After pelvic lymphadenectomy, metastatic involvement of non-SLN was diagnosed in 7 patients (13.5%). Patients with metastatic non-SLN were older (51.9 vs. 40.8 years, p = 0.01), had more often lympho-vascular space invasion (LVSI) (85.7% vs. 35.6%, p = 0.03), and had more often parametrial involvement (42.9% vs. 6.7%, p = 0.003). Multivariate analysis retained age (OR = 1.16, 95% IC = [1.01-1.32], p = 0.03) and LVSI (OR = 25.97, 95% IC = [1.16-582.1], p = 0.04) as independently associated with non-SLN involvement. Age and LVSI seemed to be predictive of non-SLN metastasis in patients with SLN metastasis in early-stage cervical cancer. Larger cohorts are needed to confirm the results and clinical usefulness of such findings.

Fluorescent Guided Sentinel Lymph Mapping of the Oral Cavity with Fluorescent-Labeled Tilmanocept.

With the shift toward utilization of sentinel lymph node biopsy (SLNB) in oral cavity cancer, improved techniques for intraoperative sentinel node identification are needed. This study investigates the feasibility of fluorescently labeled tilmanoscept in SLNB in an oral cancer rabbit model. An animal study was designed using 21 healthy male New Zealand rabbits. Gallium-68-labeled tilmanocept labeled with IRDye800CW was injected submucosally into the buccal mucosa (n = 6) or lateral tongue (n = 7) followed by PET imaging. One hour after injection, SLNB was performed using fluorescence imaging followed by a bilateral neck dissection and sampling of non-nodal surrounding tissue. All tissues were measured for radioactivity and fluorescence. In addition, eight rabbits were injected with delayed SLNB performed 48 h after injection. Buccal injections all had ipsilateral SLN drainage and tongue injections exhibited 18.2% contralateral drainage. An average of 1.9 ± 1.0 SLN (range 1-5) were identified. In addition, an average of 16.9 ± 3.3 non-sentinel lymph nodes were removed per animal. SLNs had an average of 0.69 ± 0.60 percent-of-injected dose (%ID) compared with non-sentinel nodes with 0.012 ± 0.025 %ID and surrounding tissue with 0.0067 ± 0.015 %ID. There was 98.0% agreement between sentinel lymph nodes identified using fluorescence compared to radioactivity with Cohen's kappa coefficient of 0.879. In 48-h delayed SLNB, results were consistent with 97.8% agreement with radioactivity and Cohen's Kappa coefficient of 0.884. Fluorescence identified additional lymph nodes that were not identified by radioactivity, and with one false negative. Fluorescent-labeled Tc-99 m-tilmanocept represents a highly accurate adjunct to enhance SLNB for oral cavity cancer. N/A Laryngoscope, 134:1299-1307, 2024.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study

For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n=519), internal validation set (n=129), external test set 1 (n=296), and external test set 2 (n=44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning

BackgroundDevelop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients.MethodsFrom June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared.ResultsNSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706).ConclusionsThe ML model XGBoost can well predict NSLNM in breast cancer patients.

Survival benefit of sentinel lymph node biopsy in Asian melanoma patients.

Sentinel lymph node biopsy (SLNB) provides important prognostic information for early-stage melanomas. However, statistics regarding the survival comparison between SLNB and nodal observation in Asia, where acral lentiginous melanoma (ALM) predominates, are limited. This study aimed to identify if SLNB offered survival benefits over nodal observation in early-stage melanomas in Taiwan. The retrospective study included 227 patients who met the SLNB criteria according to the National Comprehensive Cancer Network guidelines and were treated at National Taiwan University Hospital from June 1997 to June 2021. Survival analysis was performed using Kaplan-Meier curves and Cox proportional hazards regression models. Of the study population, ALM accounted for 73.1%; 161 patients (70.9%) underwent SLNB and 66 patients (29.1%) were under nodal observation. Multivariate analysis showed significantly improved melanoma-specific survival (hazard ratio [HR], 0.6; p = .02) in the SLNB group. Among those who underwent completion lymph node dissection (CLND), the non-sentinel node positivity rate was 44.4%. Immediate CLND resulted in significantly longer melanoma-specific survival and distant-metastasis-free survival (DMFS) compared to nodal observation. (HR, 0.2; p = .01 for melanoma-specific survival. HR, 0.3; p = .046 for DMFS). In conclusion, SLNB may provide survival benefits of cutaneous melanoma over nodal observation in the Taiwanese population.

A multi-dimensional nomogram to predict non-sentinel lymph node metastases in T1-2HR+ breast cancer.

Axillary lymph node dissection (ALND) could be omitted for T1-2 breast cancer patients with 1-2 positive sentinel lymph node (SLN) after breast-conserving surgery when radiation is planned. However, whether ALND could be replaced by radiation in patients with 1-3 positive SLNs when no more non-SLN metastasis were observed after mastectomy are still controversial. The aim of our study was to develop and validate a nomogram for predicting the possibility of non-SLN metastasis in T1-2 and hormone receptor (HR) positive breast cancer patients with 1-3 positive SLNs after mastectomy. We retrospectively reviewed and analyzed the data including the basic information, preoperative sonographic characteristics, and pathological features in breast cancer patients with 1-3 positive SLNs in our medical center between Jan 2016 and Dec 2021. The Chi-square, Fisher's exact test, and t test were used for comparison of categorical and qualitative variables among patients with or without non-SLN metastasis. Univariate and multivariate logistic regression were used to determine the risk factors for non-SLN metastasis. These predictors were used to build the nomogram. The C-index and area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model. A total of 49 in 107 (45.8%) patients were identified with non-SLN metastasis. In multivariate analysis, four variables including younger age, lower estrogen receptor (ER) expression, higher histological score, and cortex thickening of the lymph nodes were determined to be significantly associated with non-SLN metastasis. An individualized nomogram was consequently established with a favorable C-index of 0.822 and verified via two internal validation cohorts. The current study developed a nomogram predicting non-SLN metastasis for T1-2 and HR+ breast cancer with 1-3 positive SLNs after mastectomy and found that patients in the high-risk group exhibited worse relapse-free survival. The novel nomogram may further help surgeons to determine whether ALND could be omitted when 1-3 positive SLNs were observed in T1-2 and HR+ breast cancer patients.

Radioisotope-Guided Excision of Mediastinal Lymph Nodes in Patients with Non-Small Cell Lung Carcinoma: Feasibility and Clinical Impact.

Intraoperative localisation of nodal disease in non-small cell lung cancer (NSCLC) can be challenging. Lymph node localisation via radiopharmaceuticals is used in many conditions; we tested the feasibility of this approach in NSCLC. NSCLC patients were prospectively recruited. Intraoperative peri-tumoral injections of [99mTc]Tc-albumin nanocolloids were performed, followed by removing the tumour and locoregional lymph nodes. These were examined ex vivo with a gamma probe and labelled sentinel lymph nodes (SLNs) if they showed any activity or non-sentinel lymph nodes (nSLNs) if they did not. Thereafter, the surgical field was scanned with the probe; any further radioactive lymph node was removed and labelled as "extra" SLNs (eSLNs). All specimens were sent to histology, and metastatic status was recorded. 48 patients were enrolled, and 290 nodal stations were identified: 179 SLNs, 87 nSLNs, and 24 eSLNs. A total of 44 nodal metastases were identified in 22 patients, with 36 of them (82%) located within SLNs. Patients with nSLNs metastases had at least a co-existing positive SLN. No metastases were found in eSLNs. The technique shows high sensitivity for intraoperative nodal metastases identification. This information could allow selective lymphadenectomies in low-risk patients or more aggressive approaches in high-risk patients.

An improved nomogram including elastography for the prediction of non-sentinel lymph node metastasis in breast cancer patients with 1 or 2 sentinel lymph node metastases.

The rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. A total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram. The multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA. We conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

A nomogram for intraoperatively predicting non-sentinel lymph node metastases in early breast cancer patients with positive sentinel lymph nodes.

Individualized decisions are required in early-stage breast cancer patients. We aimed to establish a novel model for predicting non-sentinel lymph node (SLN) metastases in patients with positive SLNs, using preoperative and intraoperative characteristics and inflammatory indicators. The data of 489 patients with invasive breast cancer were retrospectively collected from Xuanwu Hospital between 2014 and 2021. Among them, 96 patients with at least one positive SLN were used to build the predictive model. Univariate and multivariate analyses were performed to identify the risk factors of non-SLN metastases. A nomogram was developed using these risk factors and was validated by calibration curves. The area under the receiver operating characteristics curve (AUC) and decision curve analyses (DCA) were used to compare our novel nomogram with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram. Cross-validation was performed for further internal validation of the predictive model. External validation was conducted using another treatment group (n=46 patients) in Xuanwu Hospital. Non-SLN metastases occurred in 42 of the 83 patients with positive SLNs (50.6%). Multivariate stepwise logistic regression indicated that the risk factors were age (P=0.032), number of positive SLNs (P=0.020), number of negative SLNs (P=0.011), resected tumor size (P=0.038), and monocyte count (P=0.012). A predictive model was developed and virtualized by nomogram using these five risk factors. The AUC of our nomogram was 0.867, which was significantly higher than that of the MSKCC model. DCA also showed a superior clinical value for our novel nomogram. After 10-fold cross-validation with 400 times repetitions, the AUC of our model was still 0.830. External validation of our model showed an AUC of 0.727. The model was well-calibrated in the internal and external validation series. A five-factor nomogram was developed for predicting non-SLN metastases in early-stage breast cancer patients. This novel tool exhibited good accuracy and could assist clinicians with intraoperative decisions in breast cancer patients with positive SLNs.

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.

Regional lymphadenectomy in the treatment of patients with melanoma skin with metastases in the sentinel lymph nodes

Regional recurrences are one of the most common first manifestations of progression of clinically localized cutaneous melanoma. They are caused by subclinical metastases in the lymph nodes. Biopsy of the sentinel lymph nodes is the most effective method of detection of hidden regional metastases. It allows to appropriately increase the volume of surgical treatment prior to dissection of the affected lymph collector and perform adjuvant therapy with the most effective drugs. This, in its turn, allows for significant improvement of regional and systemic control of the disease and long-term treatment outcomes.Data obtained in several randomized trials on the effectiveness of lymph node dissection in patients with sentinel lymph node metastasis showed that this procedure significantly improves regional control of the disease but does not affect long-term treatment outcomes. Additionally, some studies showed that assignment of responsibility for regional control to adjuvant therapy does not meet the expectations. Therefore, the most effective method of regional control of cutaneous melanoma is surgery. All of these studies show that it is necessary to develop criteria for identification of patients with high risk of metastases in the non-sentinel lymph nodes who require lymph node dissection for regional disease control and creation of the best conditions for adjuvant systemic therapy. This requires identification of predictors of multiple involvement of regional lymph nodes justifying indications for radical lymph node dissection. Studies in this area are being performed but their results are ambiguous and sometimes contradictory.Aim. To evaluate the effectiveness of lymph node biopsy and radical lymph node dissection in patients with cutaneous melanoma and subclinical involvement of the sentinel lymph node.

A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients.

Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patients without non-sentinel lymph node (NSLN) metastasis after ALND. Unnecessary ALND increases the risk of complications and detracts from quality of life. In this study, we expect to develop a nomogram based on genotypic and clinicopathologic factors to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients. This retrospective study collected data from 1,879 women breast cancer patients enrolled from multiple centers. Genotypic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, therapy and prognosis. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the mutational stability. The normalized polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Recursive feature elimination (RFE) based on linear discriminant analysis (LDA) was adopted to select the most useful predictive features, and RFE based on support vector machine (SVM) was used to reduce the number of SNPs. Multivariable logistic regression models (i.e., nomogram) were built for predicting NSLN metastasis. The predictive abilities of three types of model (based on only clinicopathologic information, the integrated clinicopathologic and all SNPs information, and integrated clinicopathologic and significant SNPs information) were compared. Internal and external validations were performed and the area under ROC curves (AUCs) as well as a series of evaluation indicators were assessed. 229 patients underwent SLNB followed by ALND and without any neo-adjuvant therapy, 79 among them (34%) had a positive axillary NSLN metastasis. The LDA-RFE identified the characteristics including lymphovascular invasion, number of positive SLNs, number of negative SLNs and two SNP clusters as significant predictors of NSLN metastasis. Furthermore, the SVM-RFE selected 29 significant SNPs in the prediction of NSLN metastasis. In internal validation, the median AUCs of the clinical and all SNPs combining model, the clinical and 29 significant SNPs combining model, and the clinical model were 0.837, 0.795 and 0.708 respectively. Meanwhile, in external validation, the AUCs of the three models were 0.817, 0.815 and 0.745 respectively. We present a new nomogram by combining genotypic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLN metastasis in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations.