Sentinel Lymph Node Metastasis In Patients Research Articles

Risk factors and prognosis of sentinel lymph node metastasis in breast-conserving breast cancer: A retrospective study based on the SEER database.

At present, the risk factors and prognosis of sentinel lymph node metastasis (SLNM) are analyzed based on the study of axillary lymph node metastasis, but whether there is a difference between the two is unclear. Therefore, an accurate and appropriate predictive model needs to be proposed to evaluate patients undergoing sentinel lymph node biopsy (SLNB) for breast cancer. We selected 16983 women with breast cancer from the Surveillance Epidemiology and End Results (SEER) database. They were randomly assigned to two cohorts, one for development (n = 11891) and one for validation (n = 5092). multi-factor logistics regression was used to distinguish risk factors affecting SLNM. The potential prognostic factors were identified using the COX regression analysis. The hazard ratio (HR) and 95% confidence interval (95%CI) were calculated for all results. Multiple Cox models are included in the nomogram, with a critical P value of .05. In order to evaluate the model's performance, Concordance index and receiver operating characteristic curves were used. Six independent risk factors affecting SLNM were screened out from the Logistic regression, including tumor location, number of regional lymph nodes (2-5), ER positive, PR positive, tumor size (T2-3), and histological grade (Grade II-III) are independent risk factors for SLNM in patients (P < .05). Eight prognostic factors were screened out in the multivariate COX regression analysis (P < .05): Age: Age 60 to 79 years, Age ≥ 80 years; Race; Histological grading: Grade II, Grade III; No radiotherapy; Tumor size: T2, T3; ER positive:, sentinel lymph node positive, married. Histological grade, tumor location, T stage, ER status, PR status and the number of SLNB are significantly correlated with axillary SLNM. Age, ethnicity, histological grade, radiotherapy, tumor size, ER status, SLN status, and marital status were independent risk factors for Breast cancer specific survival (BCSS). Moreover, the survival rate of patients with 3 positive SLNs was not significantly different from that with one or two positive SLNs, We concluded that patients with stage N1 breast cancer were exempt from axillary lymph node dissection, which is worthy of further study.

Sentinel lymph node assessment in patients with atypical endometrial hyperplasia: a systematic review and meta-analysis

ObjectiveThis systematic review and meta-analysis aimed to assess the rate of sentinel lymph node (SLN) metastases in patients with a pre-operative diagnosis of atypical hyperplasia/endometrial intra-epithelial neoplasia and endometrial cancer...

Molybdenum target mammography-based prediction model for metastasis of axillary sentinel lymph node in early-stage breast cancer.

Sentinel lymph node (SLN) status is closely related to axillary lymph node metastasis in breast cancer. However, SLN biopsy has certain limitations due to invasiveness and diagnostic efficiency. This study aimed to develop a model to predict the risk of axillary SLN metastasis in early-stage breast cancer based on mammography, a noninvasive, cost-effective, and potential complementary way. Herein, 649 patients with early-stage breast cancer (cT1-T2) who received SLN biopsy were assigned to the training cohort (n = 487) and the validation cohort (n = 162). A prediction model based on specific characteristics of tumor mass in mammography was developed and validated with R software. The performance of model was evaluated by receiver operating characteristic curve, calibration plot, and decision curve analysis. Tumor margins, spicular structures, calcification, and tumor size were independent predictors of SLN metastasis (all P < .05). A nomogram showed a satisfactory performance with an AUC of 0.829 (95% CI = 0.792-0.865) in the training cohort and an AUC of 0.825 (95% CI = 0.763-0.888) in validation cohort. The consistency between model-predicted results and actual observations showed great Hosmer-Lemeshow goodness-of-fit (P = .104). Patients could benefit from clinical decisions guided by the present model within the threshold probabilities of 6% to 84%. The prediction model for axillary SLN metastasis showed satisfactory discrimination, calibration abilities, and wide clinical practicability. These findings suggest that our prediction model based on mammography characteristics is a reliable tool for predicting SLN metastasis in patients with early-stage breast cancer.

Abstract P5-01-03: Evaluation of novel diagnostic kits using the semi-dry dot-blot method combined with an automatic reader for detecting metastases in sentinel lymph nodes of patients with breast cancer: a multi-center prospective study

Abstract Background: The semi-dry dot-blot (SDB) method, a diagnostic procedure for detecting lymph node (LN) metastases using an anti-cytokeratin (CK) antibody, is based on the theory that epithelial components, such as CK, are not found in normal LNs. Thus, metastases are diagnosed according to the presence of CK in the lavage fluid of sectioned LNs. We prospectively evaluated novel SDB kits that use a newly developed anti-CK19 antibody and an automatic reader for diagnosing sentinel LN metastases in patients with breast cancer as a multi-center study. Methods: We obtained 924 sentinel LNs dissected from 405 patients with breast cancer between January 2021 and December 2021 at six institutes in Japan. We excluded patients who underwent neoadjuvant chemotherapy and neoadjuvant endocrine therapy. LNs were sectioned at 2-mm intervals and washed with phosphate-buffered saline. Cells suspended in the lavage fluid of sectioned LNs were centrifuged and lysed to extract protein. The extracted protein was applied to the SDB kit to diagnose LN metastasis using an automatic absorbance reader. Hematoxylin and eosin (H&amp;E) stained and washed LNs were blindly examined by pathologists using intraoperative and permanent histological examination. Diagnoses based on SDB kit and automatic reader findings were compared with diagnoses made by permanent histological examination of paraffin-embedded H&amp;E-stained sections of LNs. Primary endpoints were the sensitivity, specificity, and overall agreement of the SDB kit for distinguishing macrometastases from non-macrometastases. Results: Ninety-four of the 924 LNs were assessed as macrometastases, 40 as micrometastases, and 790 as isolated tumor cells by histological examination. Compared with patients with non-macrometastases, those with macrometastases had significantly younger age (p&amp;lt; 0.01), larger primary tumor (p&amp;lt; 0.01), higher nuclear grade (p=0.04), increased lymphatic invasion (p&amp;lt; 0.01), and increased venous invasion (p=0.01). Using a borderline CK19 absorbance of 11.9 milli-absorbance for detecting macrometastases with an area under the curve of 0.989, the sensitivity, specificity, and overall agreement of the SDB kit were 94.7%, 98.3%, and 97.9%, respectively. Moreover, the sensitivity, specificity, and overall agreement of the intraoperative histological examination compared with permanent histological examination for distinguishing macrometastases from non-macrometastases were 91.4%, 99.1%, and 98.3%, respectively. Furthermore, the kits and automatic reader yielded diagnoses within approximately 20 min at a cost of &amp;lt; 30 USD for the SDB kit and &amp;lt; 3,000 USD for the automatic reader. Conclusions: The kits with an automatic reader used in our study were accurate, quick, and cost-effective in diagnosing LN metastases without the loss of LN tissue and were particularly useful for distinguishing macrometastases. We plan to make the SDB kit and automatic reader commercially available worldwide soon. Citation Format: Megumi Matsumoto, Masaaki Baba, Aya Tanaka, Sayaka Kuba, Michi Morita, Fujiko Kaseida, Hiroko Hayashi, Shigeto Maeda, Hiroshi Yano, Eiko Inamasu, Akiko Ogiya, Kenta Sekiya, Masahiro Nakashima, Hideki Ikari, Shinji Ohno, Hiroyuki Takei, Katsunori Yanagihara, Susumu Eguchi, Takeshi Nagayasu, Ryota Otsubo. Evaluation of novel diagnostic kits using the semi-dry dot-blot method combined with an automatic reader for detecting metastases in sentinel lymph nodes of patients with breast cancer: a multi-center prospective study [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-01-03.

A model for the early identification of sentinel lymph node metastasis in patients with breast cancer based on contrast-enhanced ultrasound and clinical features.

The present study was designed to establish a model for the early identification of sentinel lymph node (SLN) metastasis in patients with breast cancer (BC). The SLN metastasis predictive model was established with a retrospective training set of 365 patients with BC and was re-evaluated using a prospective validation set of 402 patients with BC. The multivariable analysis indicated that the tumor diameter [odds ratio (OR), 1.189; 95% confidence interval (CI), 1.124-1.257; P<0.001], menopause (OR, 1.011; 95% CI, 0.603-1.436; P<0.001), estrogen receptor (ER) expression (OR, 3.199; 95% CI, 1.077-6.567; P=0.043) and contrast-enhanced ultrasonography (CEUS) type (OR, 10.563; 95% CI, 6.890-28.372; P<0.001) were independent predictors of SLN status in patients with BC. The SLN metastasis predictive model was as follows: (0.173 × tumor diameter)-(4.490 × menopause) + (2.322 × ER) + (5.445 × CEUS type)-1.9521. In the training set, the model was highly sensitive (83.6%) and specific (94.3%) for the early identification of SLN metastasis. Similarly, in the validation set, the model was highly sensitive (70.4%) and specific (89.5%) for the early identification of SLN metastasis in patients with BC. Overall, in the present study, a model was successfully established to predict SLN metastasis in patients with BC that includes tumor diameter, menopausal status, ER expression and CEUS detection.

Predicting Sentinel Node Positivity in Patients with Melanoma: a Comparison of Nomograms

Background: While the risks associated with sentinel lymph node (SLN) biopsy in the regional nodal staging for malignant melanoma are low, efforts have been made at identifying patients in whom the procedure could safely be omitted. Clinicopathological parameters can be used to estimate patients at a higher probability of sentinel nodal metastases. The aim of this study is to compare the predictive value of the Memorial Sloan Kettering Cancer Center(1)(MSKCC) nomogram and the newly developed Melanoma Insitute Australia(2) (MIA) nomogram to predict SLN metastases in patients with primary cutaneous melanoma in a university teaching hospital.

Prediction of Sentinel Lymph Node Metastasis in Breast Ductal Carcinoma In Situ Diagnosed by Preoperative Core Needle Biopsy.

PurposeThe positivity of sentinel lymph node (SLN) metastasis is relatively low in ductal carcinoma in situ (DCIS) patients. The aim of this study was to investigate factors associated with SLN metastasis and build a model to predict the potential risk of SLN metastasis in patients with a preoperative diagnosis of DCIS.Patients and MethodsCore needle biopsy-proved DCIS patients who underwent SLN biopsy and breast surgery were retrospectively reviewed and selected. Univariate analysis was used to identify the variables correlated with SLN metastasis. A model to predict SLN metastasis was developed using a multivariate logistic regression in the training set and then validated in an internal set.ResultsA total of 407 patients with a preoperative diagnosis of DCIS were included. Upstaging to invasive/microinvasive cancer occurred in 225 patients after surgery. SLN metastasis was found in 42 patients, including 32 patients upstaging to invasive disease, 8 to microinvasive disease, and 2 pure DCIS. Tumor size based on US examination, axillary ultrasound finding, multifocality, surgery, upstaging, and Ki-67 expression were significantly related to SLN metastasis. The model incorporating tumor size, axillary ultrasound finding and multifocality yielded an AUC of 0.805 (95% CI: 0.715–0.895, p<0.001) in the training set, and 0.729 (95% CI: 0.547–0.911, p=0.013) in the testing set.ConclusionA simple model was developed to predict SLN metastasis in patients with a preoperative diagnosis of DCIS. With good discriminatory power, this model should be helpful for surgeons to decide if SLN biopsy could be safely avoided in certain patients.

Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer

BackgroundTo establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer.MethodsA total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models.ResultsSeven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer.ConclusionsThe model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.

Prediction of Sentinel Lymph Node Metastasis Using the Platelet-to-lymphocyte Ratio in T1 Breast Cancer.

The host's systemic inflammatory response is thought to affect the progression of cancer and the antitumor effects of chemotherapy. Meta-analyses have reported that the peripheral blood platelet-to-lymphocyte ratio (PLR) is a prognostic indicator of this effect. Therefore, we hypothesized that PLR may differ, depending on sentinel lymph node metastasis (SLNM) in patients diagnosed with cT1N0M0 breast cancer by preoperative imaging. This study investigated the ability of preoperative PLR to predict SLNM in patients diagnosed with cT1N0M0 breast cancer. This study included 475 patients with cT1N0M0 breast cancer diagnosed by preoperative imaging. Peripheral blood was obtained at diagnosis, i.e., before surgery. PLR was calculated from preoperative blood tests, by dividing the absolute platelet count by the absolute lymphocyte count. The probability of SLNM was significantly higher (p=0.002) in cases where the tumor diameter was larger than 10 mm. The incidence of SLNM was significantly high in the high (preoperative) PLR group (p=0.031). Multivariate analysis revealed that high PLR [compared to low PLR, p=0.021, odds ratio (OR)=1.815, 95% confidence interval (CI)=1.093-3.090] and large tumor size (compared to small tumor size, p=0.001, OR=2.688, 95%CI=1.524-4.997) were independent factors influencing SLNM. PLR may act as a predictor of SLNM in cT1N0M0 breast cancer.

Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

To evaluate the noninvasive predictive performance of deep learning features based on staging CT for sentinel lymph node (SLN) metastasis of breast cancer. A total of 348 breast cancer patients were enrolled in this study, with their SLN metastases pathologically confirmed. All patients received contrast-enhanced CT preoperative examinations and CT images were segmented and analyzed to extract deep features. After the feature selection, deep learning signature was built with the selected key features. The performance of the deep learning signatures was assessed with respect to discrimination, calibration, and clinical usefulness in the primary cohort (184 patients from January 2016 to March 2017) and then validated in the independent validation cohort (164 patients from April 2017 to December 2018). Ten deep learning features were automatically selected in the primary cohort to establish the deep learning signature of SLN metastasis. The deep learning signature shows favorable discriminative ability with an area under curve of 0.801 (95% confidence interval: 0.736-0.867) in primary cohort and 0.817 (95% confidence interval: 0.751-0.884) in validation cohort. To further distinguish the number of metastatic SLNs (1-2 or more than two metastatic SLN), another deep learning signature was constructed and also showed moderate performance (area under curve 0.770). We developed the deep learning signatures for preoperative prediction of SLN metastasis status and numbers (1-2 or more than two metastatic SLN) in patients with breast cancer. The deep learning signature may potentially provide a noninvasive approach to assist clinicians in predicting SLN metastasis in patients with breast cancer.

Minimal prognostic significance of sentinel lymph node metastasis in patients with cT1\u20132 and cN0 breast cancer

BackgroundThe prognostic value of sentinel lymph node (SLN) metastases may be minimized by the limited disease burden of lymph node metastases and tailoring adjuvant therapy based on breast cancer biology. The aim of this study is to assess the prognostic significance of SLN metastasis in patients with cT1–2N0M0 breast cancer.Patients and methodsBetween January 2006 and December 2015, 582 patients underwent SLN biopsy for cT1–2N0M0 breast cancers. cN0 was essentially diagnosed by ultrasound sonography. The prognostic values of SLN metastases were retrospectively evaluated.ResultsAmong 582 patients with cT1–2N0M0 breast cancer, 111 patients (19.1%) were positive for SLN metastasis, including 39 cases (6.7%) of micrometastasis and 72 cases (12.4%) of macrometastases. The median size of SLN metastasis was 3.0 mm (range 0.2–16 mm, mean 4.1 mm). In log-rank test, presence of SLN metastasis was not associated with breast cancer recurrence (p = 0.21); 5-year and 10-year recurrence-free survival (RFS) were 93.0% and 96.5%, and 93.0% and 90.4% in the SLN-positive and SLN-negative groups, respectively. In the propensity score matching cohort (n = 178), there was no significant difference in RFS between the SLN-positive and SLN-negative groups (p = 0.90). In Cox regression analysis, a continuous value of Ki67 expression was a significant prognostic factor (HR 1.03; 95% CI 1.01–1.05, p = 0.017).ConclusionSLN metastasis has a minimal impact on RFS for patients with cT1–2N0M0 breast cancer in the modern medical era. A proliferation marker is a better factor for poor prognosis than the presence of SLN metastases in this population.

Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer

Breast cancer is the most common female malignancy among women. Sentinel lymph node (SLN) status is a crucial prognostic factor for breast cancer. In this paper, we propose an integrated scheme of deep learning and bag-of-features (BOF) model for preoperative prediction of SLN metastasis. Specifically, convolution neural networks (CNNs) are used to extract deep features from the three 2D representative orthogonal views of a segmented 3D volume of interest. Then, we use a BOF model to furtherly encode the all deep features, which makes features more compact and products high-dimension sparse representation. In particular, a kernel fusion method that assembles all features is proposed to build a discriminative support vector machine (SVM) classifier. The bag of deep feature model is evaluated using the diffusion-weighted magnetic resonance imaging (DWI) database of 172 patients, including 74 SLN and 98 non-SLN. The results show that the proposed method achieves area under the curve (AUC) as high as 0.852 (95% confidence interval (CI): 0.716–0.988) at test set. The results demonstrate that the proposed model can potentially provide a noninvasive approach for automatically predicting prediction of SLN metastasis in patients with breast cancer.

241 (PB-036) - Risk factors for sentinel node metastases in patients with ductal carcinoma in situ in the breast

241 (PB-036) - Risk factors for sentinel node metastases in patients with ductal carcinoma in situ in the breast

Detection of sentinel lymph node in patients with breast cancer: Application of 3D contrast enhanced ultrasound (3D-CEUS).

e12095 Background: Sentinel lymph node biopsy (SLNB) has became widely accepted as the gold standard procedure for axilla nodal staging .The main methods for the detection of Sentinel lymph nodes (SLNs) are dye-guided and g-probe-guided method.Currently, studies suggest that 2D - Contrast Enhanced Ultrasound(2D-CEUS) may be as good as the accepted techniques for SLNs detection .But the 2D-CEUS can not image the spatial relationship of the lymph vessel and SLNs.The objective of this study was to explore whether three-dimensional contrast enhanced ultrasonography (3D-CEUS) poses higher diagnostic value of in SLNs and lymphatic drainage routes of breast cancer than conventional ultrasonography (US) or 2D-CEUS.To our knowledge, this is the first study to investigate the value of 3D-CEUS in the preoperative diagnosis of SLN metastases in patients with early breast cancer. Methods: By using postoperative pathological results as gold standard, the accuracy and sensitivity of conventional US, 2D-CEUS and 3D-CEUS for SLNs were retrospectively tested in 73 patients with early breast cancer confirmed by pathology between December 2015 and June 2016, all patients underwent US, 2D-CEUS and 3D-CEUS before operation. Injection of the Blue dye was performed half an hour before the operation. Results: According to the postoperative pathology, 25 of 73 patients were with lymphatic metastasis. The lymph nodes detection rate of 2D-US,2D-CEUS and 3D-CEUS was 35.2%, 73.4%, 79.4% ,respectively.The correct rate of 2D-US,2D-CEUS and 3D-CEUS was 74%,90.4%,91.8%, respectively(p&lt;0.05).The lymph drainage route of SLNs are divided into 7 types by 3D-CEUS and the enhancement modes of the lymphatic vessels and the lymph nodes are divided into six categories by 2D-CEUS. Conclusions: The 2D-CEUS and 3D-CEUS can clearly display the SLNs and the lymph drainage routes. 3D-CEUS possesses advantages over 2D-CEUS in displaying lesions with spatial relationship which is informative to preoperative assessment of the stage, the operative type and surgical prognosis in patients with breast cancer, and can contribute greatly to the easy detection of SLNs during SLNB. Clinical trial information: Approval NO,SCPPHEC-D-2015-75.

One-step nucleic acid amplification (OSNA) for the detection of sentinel lymph node metastasis in endometrial cancer

ObjectiveTo evaluate the efficacy of one-step nucleic acid amplification (OSNA) for the diagnosis of sentinel lymph node (SLN) metastasis compared with histopathological examination in patients with endometrial carcinoma. MethodsA total of 94 SLNs from 34 patients with endometrial carcinoma were enrolled. The central 1-mm portion of each node was subjected to semi-serial sectioning, sliced at 200-μm intervals and examined by hematoxylin and eosin and cytokeratin 19 (CK19) immunohistochemical staining, and the remaining tissue was analysed by OSNA using CK19 mRNA. The accuracy of the OSNA assay was evaluated based on histopathological diagnosis. ResultsHistologically, 89 SLNs were determined to be metastasis negative, and the remaining five SLNs were metastasis positive. Using the breast cancer cutoff value for detecting lymph node metastasis (OSNA criteria for breast cancer, >250copies/μl) the sensitivity of the OSNA assay was 100%; specificity was 87.6%; diagnostic accuracy was 88.3%. Discordant results were recorded for 11 of 94 SLNs. In all 11 cases, a positive result was given by the OSNA assay but not by histopathological examination. In two SLNs from the same patient, histopathological examination revealed the presence of benign epithelial inclusions that were CK19 positive; both SLNs yielded a positive result in the OSNA assay (true–false positive). All remaining nine histologically-negative/OSNA-positive SLNs were classified as micrometastasis (+) by the OSNA assay. ConclusionThe OSNA assay shows high sensitivity and specificity, which suggests its utility as a novel tool for the molecular detection of SLN metastasis in patients with endometrial carcinoma.

Sentinel Lymph Node Biopsy in Thin Cutaneous Melanoma: A Systematic Review and Meta-Analysis

Most patients with melanoma have a thin (≤1.00mm) lesion. There is uncertainty as to which patients with thin melanoma should undergo sentinel lymph node (SN) biopsy. We sought to quantify the proportion of SN metastases in patients with thin melanoma and to determine the pooled effect of high-risk features of the primary lesion on SN positivity. Published literature between 1980 and 2015 was searched and critically appraised. Primary outcome was the proportion of SN metastases in patients with thin cutaneous melanoma. Secondary outcomes included the effect of high-risk pathological features of the primary lesion on the proportion of SN metastases. Summary measures were estimated by Mantel-Haenszel method using random effects meta-analyses. Sixty studies (10,928 patients) met the criteria for inclusion. Pooled SN positivity was 4.5% [95% confidence interval (CI) 3.8-5.2%]. Predictors of a positive SN were: thickness ≥0.75mm [adjusted odds ratio (AOR) 1.90 (95% CI 1.08-3.34); with a likelihood of SN metastases of 8.8% (95% CI 6.4-11.2%)]; Clark level IV/V [AOR 2.24 (95% CI 1.23-4.08); with a likelihood of 7.3% (95% CI 6.2-8.4%)]; ≥1 mitoses/mm2 [AOR 6.64 (95% CI 2.77-15.88); pooled likelihood 8.8% (95% CI 6.2-11.4%)]; and the presence of microsatellites [unadjusted OR 6.94 (95% CI 2.13-22.60); likelihood 26.6% (95% CI 4.3-48.9%)]. The pooled proportion of SN metastases in thin melanoma is 4.5%. Thickness ≥0.75mm, Clark level IV/V, mitoses, and microsatellites significantly increased the odds of SN positivity and should prompt strong consideration of SN biopsy.

The clinical usefulness of the intraoperative detection of sentinel lymph node metastases by a rapid RT-PCR system in patients with gastric cancer.

The incidence of pathological lymph node metastases in patients with gastric cancer is 5% to 10%, which means that approximately 90% of patients with gastric cancer may undergo unnecessary lymphadenectomy. The precise intraoperative diagnosis of sentinel lymph node (SN) metastases is essential. The purpose of the current study was to verify the usefulness of a rapid reverse transcriptase-polymerase chain reaction (RT-PCR) system compared with hematoxylin and eosin staining for such diagnoses. A total of 113 patients with clinical T1-T2 (cT1-T2) gastric cancer, including 73 patients with cT1cN0 disease with a tumor diameter <4 cm, were enrolled in the current study. SNs were identified by a radioisotope method. Carcinoembryonic antigen and cytokeratin 19 were used as markers for RT-PCR and the cutoff values were set using 1701 lymph nodes harvested from 157 patients with gastric cancer. SNs were detected in all 113 patients. Sensitivity and accuracy for detection by paraffin section were both 100% in patients with cT1 disease and were 60% and 90%, respectively, in patients with cT2 disease. The sensitivity of RT-PCR for the detection of pathological SN metastases was 92.3%. Furthermore, 11 patients had SN metastases detected only by RT-PCR, and these patients had frequent lymphatic invasion. Hematoxylin and eosin staining detected SN metastases in 6 of 73 patients with cT1cN0 gastric cancer; RT-PCR and frozen section detected SN metastases in 6 and 4 of these patients, respectively. Accordingly, the sensitivity of RT-PCR and frozen section for the detection of those pathological SN metastases were 100% and 66.6%, respectively. The rapid RT-PCR system appears to have clinical usefulness for the intraoperative detection of SN metastases in patients with gastric cancer.

Predictors of Sentinel Lymph Node Status in Cutaneous Melanoma: A Classification and Regression Tree Analysis

ObjectiveThe main aim of this study was to identify predictors of sentinel lymph node (SN) metastasis in cutaneous melanoma. Patients and methodsThis was a retrospective cohort study of 818 patients in 2 tertiary-level hospitals. The primary outcome variable was SN involvement. Independent predictors were identified using multiple logistic regression and a classification and regression tree (CART) analysis. ResultsUlceration, tumor thickness, and a high mitotic rate (≥6 mitoses/mm2) were independently associated with SN metastasis in the multiple regression analysis. The most important predictor in the CART analysis was Breslow thickness. Absence of an inflammatory infiltrate, patient age, and tumor location were predictive of SN metastasis in patients with tumors thicker than 2mm. In the case of thinner melanomas, the predictors were mitotic rate (>6 mitoses/mm2), presence of ulceration, and tumor thickness. Patient age, mitotic rate, and tumor thickness and location were predictive of survival. ConclusionsA high mitotic rate predicts a higher risk of SN involvement and worse survival. CART analysis improves the prediction of regional metastasis, resulting in better clinical management of melanoma patients. It may also help select suitable candidates for inclusion in clinical trials.

Factores predictivos del estado del ganglio centinela en el melanoma cutáneo: análisis mediante un árbol de clasificación y regresión

ObjectiveThe main aim of this study was to identify predictors of sentinel lymph node (SN) metastasis in cutaneous melanoma. Patients and methodsThis was a retrospective cohort study of 818 patients in 2 tertiary-level hospitals. The primary outcome variable was SN involvement. Independent predictors were identified using multiple logistic regression and a classification and regression tree (CART) analysis. ResultsUlceration, tumor thickness, and a high mitotic rate (≥6 mitoses/mm2) were independently associated with SN metastasis in the multiple regression analysis. The most important predictor in the CART analysis was Breslow thickness. Absence of an inflammatory infiltrate, patient age, and tumor location were predictive of SN metastasis in patients with tumors thicker than 2mm. In the case of thinner melanomas, the predictors were mitotic rate (>6 mitoses/mm2), presence of ulceration, and tumor thickness. Patient age, mitotic rate, and tumor thickness and location were predictive of survival. ConclusionsA high mitotic rate predicts a higher risk of SN involvement and worse survival. CART analysis improves the prediction of regional metastasis, resulting in better clinical management of melanoma patients. It may also help select suitable candidates for inclusion in clinical trials.

A breast cancer nomogram for prediction of non–sentinel-node metastasis in patients with sentinel node metastasis and validations of the 14 models.

93 Background: To avoid performing axillary lymph node dissection (ALND) for non-sentinel lymph node (SLN)-negative patients with SLN-positive axilla, nomograms for predicting the status of the axillary non-SLNs have been developed in many centers. We created a new nomogram predicting non-SLN metastasis in SLN positive patients with invasive breast cancer and evaluated 14 existing breast cancer models in our patient group. Methods: Two hundred and thirty seven invasive breast cancer patients with SLN metastases who underwent ALND were included in the present study. Based on the independent predictive factors for non-SLN metastasis identified by logistic regression analysis, we developed a new nomogram. Receiver operating characteristics (ROC) curves for the models were created, and the areas under the curves (AUC) were computed. Results: In a multivariate analysis, tumor size, presence of lymphovascular invasion, extranodal extension of SLN, large size of metastatic SLN in mm, the number of negative SLNs, and the multifocality were found to be independent predictive factors for non-SLN metastasis. The AUC was found to be 0.87 which is the highest value reported to date for previous models, and calibration was good for the present Ondokuz Mayis nomogram. Among the 14 validated models, the MSKCC, Stanford, Turkish, MD Anderson, MOU (Masaryk), Ljubljana, and 9 Eylul models yielded excellent AUC values of &gt; 0.80. Conclusions: We present a new model (the Ondokuz Mayis nomogram) to predict the likelihood of non-SLN metastasis. Each clinic should determine and use the most suitable nomogram or should create their own nomograms for the prediction of non-SLN metastasis. [Table: see text]