Assessment Of Lymph Node Metastasis Research Articles

Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging.

To explore diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for assessing pathological prognostic factors in patients with rectal cancer. A total of 162 patients (105 males; mean age of 61.8 ± 13.1 years old) scheduled to undergo radical surgery were enrolled in this prospective study. The pathological prognostic factors included histological differentiation, lymph node metastasis (LNM), and extramural vascular invasion (EMVI). The DWI, IVIM, and DKI parameters were obtained and correlated with prognostic factors using univariable and multivariable logistic regression. Their assessment value was evaluated using receiver operating characteristic (ROC) curve analysis. Multivariable logistic regression analyses showed that higher mean kurtosis (MK) (odds ratio (OR) = 194.931, p < 0.001) and lower apparent diffusion coefficient (ADC) (OR = 0.077, p = 0.025) were independently associated with poorer differentiation tumors. Higher perfusion fraction (f) (OR = 575.707, p = 0.023) and higher MK (OR = 173.559, p < 0.001) were independently associated with LNMs. Higher f (OR = 1036.116, p = 0.024), higher MK (OR = 253.629, p < 0.001), lower mean diffusivity (MD) (OR = 0.125, p = 0.038), and lower ADC (OR = 0.094, p = 0.022) were independently associated with EMVI. The area under the ROC curve (AUC) of MK for histological differentiation was significantly higher than ADC (0.771 vs. 0.638, p = 0.035). The AUC of MK for LNM positivity was higher than f (0.770 vs. 0.656, p = 0.048). The AUC of MK combined with MD (0.790) was the highest among f (0.663), MK (0.779), MD (0.617), and ADC (0.610) in assessing EMVI. The DKI parameters may be used as imaging biomarkers to assess pathological prognostic factors of rectal cancer before surgery. Diffusion kurtosis imaging (DKI) parameters, particularly mean kurtosis (MK), are promising biomarkers for assessing histological differentiation, lymph node metastasis, and extramural vascular invasion of rectal cancer. These findings suggest DKI's potential in the preoperative assessment of rectal cancer. Mean kurtosis outperformed the apparent diffusion coefficient in assessing histological differentiation in resectable rectal cancer. Perfusion fraction and mean kurtosis are independent indicators for assessing lymph node metastasis in rectal cancer. Mean kurtosis and mean diffusivity demonstrated superior accuracy in assessing extramural vascular invasion.

DNA methylation biomarkers for predicting lymph node metastasis in colorectal cancer.

Colorectal cancer is one of the most common cancers worldwide. Lymph node metastasis is an important marker of colorectal cancer progression and plays a key role in the evaluation of patient prognosis. Accurate preoperative assessment of lymph node metastasis is crucial for devising appropriate treatment plans. However, current clinical imaging methods have limitations in many aspects. Therefore, the discovery of a method for accurately predicting lymph node metastasis is crucial clinical decision-making. DNA methylation is a common epigenetic modification that can regulate gene expression, which also has an important impact on the development of colorectal cancer. It is considered to be a promising biomarker with good specificity and stability and has promising application in predicting lymph node metastasis in patients with colorectal cancer. This article reviews the characteristics and limitations of currently available methods for predicting lymph node metastasis in patients with colorectal cancer and discusses the role of DNA methylation as a biomarker.

Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

ObjectivesTo construct and validate multiparametric MR-based radiomic models based on primary tumors for predicting lymph node metastasis (LNM) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients.MethodsA total of 150 LARC patients from two independent centers were enrolled. The training cohort comprised 100 patients from center A. Fifty patients from center B were included in the external validation cohort. Radiomic features were extracted from the manually segmented volume of interests of the primary tumor before and after nCRT. Feature selection was performed using multivariate logistic regression analysis. The clinical risk factors were selected via the least absolute shrinkage and selection operator method. The radiologist’s assessment of LNM was performed. Eight models were constructed using random forest classifiers, including four single-sequence models, three combined-sequence models, and a clinical model. The models’ discriminative performance was assessed via receiver operating characteristic curve analysis quantified by the area under the curve (AUC).ResultsThe AUCs of the radiologist’s assessment, the clinical model, and the single-sequence models ranged from 0.556 to 0.756 in the external validation cohort. Among the single-sequence models, modelpost_DWI exhibited superior predictive power, with an AUC of 0.756 in the external validation set. In combined-sequence models, modelpre_T2_DWI_post had the best diagnostic performance in predicting LNM after nCRT, with a significantly higher AUC (0.831) than those of the clinical model, modelpre_T2_DWI, and the single-sequence models (all p < 0.05).ConclusionsA multiparametric model that incorporates MR radiomic features before and after nCRT is optimal for predicting LNM after nCRT in LARC.Critical relevance statementThis study enrolled 150 LARC patients from two independent centers and constructed multiparametric MR-based radiomic models based on primary tumors for predicting LNM following nCRT, which aims to guide therapeutic decisions and predict prognosis for LARC patients.Key PointsThe biological characteristics of primary tumors and metastatic LNs are similar in rectal cancer.Radiomics features and clinical data before and after nCRT provide complementary tumor information.Preoperative prediction of LN status after nCRT contributes to clinical decision-making.Graphical

Advanced Gastric Cancer: CT Radiomics Prediction of Lymph Modes Metastasis After Neoadjuvant Chemotherapy.

This study aims to create and assess machine learning models for predicting lymph node metastases following neoadjuvant treatment in advanced gastric cancer (AGC) using baseline and restaging computed tomography (CT). We evaluated CT images and pathological data from 158 patients with resected stomach cancer from two institutions in this retrospective analysis. Patients were eligible for inclusion if they had histologically proven gastric cancer. They had received neoadjuvant chemotherapy, with at least 15 lymph nodes removed. All patients received baseline and preoperative abdominal CT and had complete clinicopathological reports. They were divided into two cohorts: (a) the primary cohort (n = 125) for model creation and (b) the testing cohort (n = 33) for evaluating models' capacity to predict the existence of lymph node metastases. The diagnostic ability of the radiomics-model for lymph node metastasis was compared to traditional CT morphological diagnosis by radiologist. The radiomics model based on the baseline and preoperative CT images produced encouraging results in the training group (AUC 0.846) and testing cohort (AUC 0.843). In the training cohort, the sensitivity and specificity were 81.3% and 77.8%, respectively, whereas in the testing cohort, they were 84% and 75%. The diagnostic sensitivity and specificity of the radiologist were 70% and 42.2% (using baseline CT) and 46.3% and 62.2% (using preoperative CT). In particular, the specificity of radiomics model was higher than that of conventional CT in diagnosing N0 cases (no lymph node metastasis). The CT-based radiomics model could assess lymph node metastasis more accurately than traditional CT imaging in AGC patients following neoadjuvant chemotherapy.

Systematic Review-Role of MRI in Cervical Cancer Staging.

A systematic review of the diagnostic accuracy of MRI in the staging of cervical cancer was conducted based on the literature from the last 5 years. A literature search was performed in the Cochrane Library, EMBASE, MEDLINE and PubMed databases using the MeSH terms "cervical cancer", "MRI" and "neoplasm staging". A total of 110 studies were identified, of which 8 fit the inclusion criteria. MRI showed adequate accuracy (74-95%) and high sensitivity (92-100%) in assessing stromal invasion. The data for MRI in terms of assessing vaginal and pelvic side wall involvement were wide ranging and inconclusive. In assessing lymph node metastasis, MRI showed an adequate accuracy (73-90%), specificity (75-91%) and NPV (71-96%) but poor sensitivity (52-75%) and PPV (52-75%). MRI showed high accuracy (95%), sensitivity (78-96%), specificity (87-94%), and NPV (98-100%) but poor PPV (27-42%) in detecting bladder involvement. There was a paucity of data on the use of MRI in assessing rectal involvement in cervical cancer. Overall, the literature was heterogenous in the definitions and language used, which reduced the comparability between articles. More research is required into the diagnostic accuracy of MRI in the staging of cervical cancer and there must be increased consistency in the definitions and language used in the literature.

Establishment and validation of a risk score model based on EUS: assessment of lymph node metastasis in early gastric cancer

Lymph node metastasis significantly affects the prognosis of early gastric cancer patients. Endoscopic ultrasonography (EUS) plays a crucial role in the preoperative assessment of early gastric cancer. This study evaluated the efficacy of EUS in identifying lymph node metastasis in early gastric cancer patients and developed a risk score model to aid in choosing the best treatment options. We retrospectively analyzed the effectiveness of EUS for detecting lymph node metastasis in early gastric cancer patients. A risk score model for predicting lymph node metastasis preoperatively was created using independent risk factors identified through binary logistic regression analysis and subsequently validated. Receiver operating characteristic (ROC) curves were generated for both the development and validation cohorts. The overall accuracy of EUS in identifying lymph node metastasis was 85.3%, although its sensitivity (29.2%) and positive predictive value (38.7%) were relatively low. Patients were categorized based on preoperative risk factors for lymph node metastasis, including tumor size ≥20 mm, lymph nodes ≥10 mm, BMI ≥24 kg/m2, and lymph node metastasis on CT scans. A 7-point risk score model was developed to assess the likelihood of lymph node metastasis. The areas under the ROC curve (AUCs) for the development and validation sets were 0.842 and 0.837, respectively, with sensitivities of 64% and 79%, respectively. We developed a practical risk score model based on preoperative factors to help EUS predict lymph node metastasis in early gastric cancer patients, guiding the selection of optimal treatment approaches for these patients.

The predictive value of serum NLR, SII, and OPNI for lymph node metastasis in breast cancer patients with internal mammary lymph nodes after thoracoscopic surgery.

In this research, we delved into the predictive potential of three key markers - the neutrophil-to-lymphocyte ratio (NLR), systemic immune inflammation index (SII), and Onodera's prognostic nutritional index (OPNI), in assessing lymph node metastases in breast cancer patients who had internal mammary lymph node involvement following thoracoscopic surgery. Our study revealed notable pathological distinctions between the groups with and without metastases, while age, tumor size, and histological grade exhibited no significant differences. The analysis unveiled statistically significant variances in NLR, SII, and OPNI when comparing these two groups. Multivariate analysis pinpointed NLR (OR = 1.503), SII (OR = 1.987), and OPNI (OR = 0.612) as robust predictors of lymph node metastases. Remarkably, combining these markers (AUC: 0.897) substantially enhanced the precision of predicting lymph node metastases compared to individual measurements (NLR: 0.749, SII: 0.717, and OPNI: 0.787). In conclusion, this study underscores the pivotal role of NLR, SII, and OPNI in predicting lymph node metastasis among breast cancer patients with internal mammary lymph node involvement post-thoracoscopic surgery, affirming our utility as reliable independent predictors of this critical clinical outcome.

Risk of lymph node metastasis in T1 esophageal adenocarcinoma: a meta-analysis.

Patients with early (T1) esophageal adenocarcinoma (EAC) are increasingly having definitive local therapy endoscopically. Endoscopic resection is not able to pathologically stage or treat lymph node metastasis (LNM). Accurate identification of patients having nodal metastasis is critical to select endoscopic therapy over surgery. This study aimed to define the risk of LNM in T1 EAC. A meta-analysis of studies of patients who underwent surgery and lymphadenectomy with assessment of LNM was performed according to PRISMA. Main outcome was probability of LNM in T1a and T1b disease. Secondary outcomes were risk factors for LNM and rate of LNM in submucosal T1b (SM1, SM2, and SM3) disease. Registered with PROSPERO (CRD42022341794). Twenty cohort studies involving 2264 patients with T1 EAC met inclusion criteria: T1a (857 patients) with 36 (4.2%) node positive and T1b (1407 patients) with 327 (23.2%) node positive. Subgroup analysis of T1b lesions was available in 10 studies (405 patients). Node positivity for SM1, SM2, and SM3 was 16.3%, 16.2%, and 29.4%, respectively. T1 substage (odds ratio [OR] 7.72, 95% confidence interval [CI] 4.45-13.38, P < 0.01), tumor differentiation (OR 2.82, 95% CI 2.06-3.87, P < 0.01), and lymphovascular invasion (OR 13.65, 95% CI 6.06-30.73, P < 0.01) were associated with LNM. T1a disease demonstrated a 4.2% nodal metastasis rate and T1b disease a rate of 23.2%. Endoscopic therapy should be reserved for T1a disease and perhaps select T1b disease, which has a moderately high rate of nodal metastasis. There were inadequate data to stratify T1b SM disease into 'low-risk' and 'high-risk' based on tumor differentiation and lymphovascular invasion.

Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review.

The accurate and timely assessment of lymph node involvement is paramount in the management of patients with malignant tumors, owing to its direct correlation with cancer staging, therapeutic strategy formulation, and prognostication. Dual-energy computed tomography (DECT), as a burgeoning imaging modality, has shown promising results in the diagnosis and prediction of preoperative metastatic lymph nodes in recent years. This article aims to explore the application of DECT in identifying metastatic lymph nodes (LNs) across various cancer types, including but not limited to thyroid carcinoma (focusing on papillary thyroid carcinoma), lung cancer, and colorectal cancer. Through this narrative review, we aim to elucidate the clinical relevance and utility of DECT in the detection and predictive assessment of lymph node metastasis in malignant tumors, thereby contributing to the broader academic discourse in oncologic radiology and diagnostic precision.

Advancing Tailored Treatments: A Predictive Nomogram, Based on Ultrasound and Laboratory Data, for Assessing Nodal Involvement in Endometrial Cancer Patients.

Assessing lymph node metastasis is crucial in determining the optimal therapeutic approach for endometrial cancer (EC). Considering the impact of lymphadenectomy, there is an urgent need for a cost-effective and easily applicable method to evaluate the risk of lymph node metastasis in cases of sentinel lymph node (SLN) biopsy failure. This retrospective monocentric study enrolled EC patients, who underwent surgical staging with nodal assessment. Data concerning demographic, clinicopathological, ultrasound, and surgical characteristics were collected from medical records. Ultrasound examinations were conducted in accordance with the IETA statement. We identified 425 patients, and, after applying exclusion criteria, the analysis included 313 women. Parameters incorporated into the nomogram were selected via univariate and multivariable analyses, including platelet count, myometrial infiltration, minimal tumor-free margin, and CA 125. The nomogram exhibited good accuracy in predicting lymph node involvement, with an AUC of 0.88. Using a cutoff of 10% likelihood of nodal involvement, the nomogram displayed a low false-negative rate of 0.04 (95% CI 0.00-0.19) in the training set. The adaptability of this straightforward model renders it suitable for implementation across diverse clinical settings, aiding gynecological oncologists in preoperative patient evaluations and facilitating the design of personalized treatments. However, external validation is mandatory for confirming diagnostic accuracy.

Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer.

The accurate assessment of lymph node metastasis (LNM) is crucial for the staging, treatment, and prognosis of lung cancer. In this study, we explored the potential value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters in the prediction of LNM in non-small cell lung cancer (NSCLC). In total, 91 patients presenting with solid solitary pulmonary nodules (8 mm < diameter ≤30 mm) with pathologically confirmed NSCLC (57 without LNM, and 34 with LNM) were enrolled in the study. The patients' basic clinical data and the SDCT morphological features were analyzed using the chi-square test or Fisher's exact test. The Mann-Whitney U-test and independent sample t-test were used to analyze the differences in multiple SDCT quantitative parameters between the non-LNM and LNM groups. The diagnostic efficacy of the corresponding parameters in predicting LNM in NSCLC was evaluated by plotting the receiver operating characteristic (ROC) curves. A multivariate logistic regression analysis was conducted to determine the independent predictive factors of LNM in NSCLC. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. There were no significant differences between the non-LNM and LNM groups in terms of age, sex, and smoking history. Lesion size and vascular convergence sign differed significantly between the two groups (P<0.05), but there were no significant differences in the six tumor markers. The SDCT quantitative parameters [SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, normalized iodine concentration (NIC) and NZeff] were significantly higher in the non-LNM group than the LNM group (P<0.05). The ROC analysis showed that CER40keV, NIC, and CER70keV had higher diagnostic efficacy than other quantitative parameters in predicting LNM [areas under the curve (AUCs) =0.794, 0.791, and 0.783, respectively]. The multivariate logistic regression analysis showed that size, λ, and NIC were independent predictive factors of LNM. The combination of size, λ, and NIC had the highest diagnostic efficacy (AUC =0.892). The interobserver repeatability of the SDCT quantitative and derived quantitative parameters in the study was good (ICC: 0.801-0.935). The SDCT quantitative parameters combined with the clinical data have potential value in predicting LNM in NSCLC. The size + λ + NIC combined parameter model could further improve the prediction efficacy of LNM.

Convenient method to improve efficiency of lymph node examination after gastrectomy with D2 lymphadenectomy for gastric cancer

BackgroundThe D2 procedure has been accepted as the standard treatment for advanced gastric cancer (GC) in East Asia. Determination of the number of lymph nodes (LNs) after gastrectomy may influence the pathological stage assessment of lymph node metastasis, significantly influencing prognostic evaluations and formulation of chemotherapy regimens.MethodsBetween January 2020 and January 2022, the medical files of 312 patients with clinical stage T0-4aN0-3M0 gastric cancer were reviewed retrospectively, and the patients were assigned to the normal group (lymph nodes were examined roughly), manual group (lymph nodes were manually examined meticulously), and device group (lymph nodes were examined by device). The clinical and pathologic characteristics, number of lymph nodes harvested, and the time required for lymph node examination was compared.ResultsA total of 312 gastric cancer patients (mean age 65.8 ± 10.3 years, 85 females and 227 males) underwent gastrectomy with curative intent at our department. Sex, age, body mass index (BMI), tumor size, clinical TNM stage, and pathologic TNM stage in the three groups showed no statistically significant differences (P > 0.05). The mean number of harvested lymph nodes in the normal, manual, and device group was 24.2, 36.6 and 35.2, respectively, which showed significant differences (P < 0.0001). The mean number of positive lymph nodes in the normal, manual, and device group was 3.5, 3.9 and 3.9, respectively (P = 0.99). The mean time consumption in device group was 15 min while the time consumption in manual group was 52.3 min, which showed a significant difference (P < 0.0001).ConclusionThis improved lymph node examination method offers a simple approach that is worth promoting, and it can improve the number of harvested lymph nodes efficiently.

Nomogram for predicting lymph node metastasis in patients with ovarian cancer using ultrasonography: a multicenter retrospective study

BackgroundOvarian cancer is a common cancer among women globally, and the assessment of lymph node metastasis plays a crucial role in the treatment of this malignancy. The primary objective of our study was to identify the risk factors associated with lymph node metastasis in patients with ovarian cancer and develop a predictive model to aid in the selection of the appropriate surgical procedure and treatment strategy.MethodsWe conducted a retrospective analysis of data from patients with ovarian cancer across three different medical centers between April 2014 and August 2022. Logistic regression analysis was employed to establish a prediction model for lymph node metastasis in patients with ovarian cancer. We evaluated the performance of the model using receiver operating characteristic (ROC) curves, calibration plots, and decision analysis curves.ResultsOur analysis revealed that among the 368 patients in the training set, 101 patients (27.4%) had undergone lymph node metastasis. Maximum tumor diameter, multifocal tumor, and Ki67 level were identified as independent risk factors for lymph node metastasis. The area under the curve (AUC) of the ROC curve in the training set was 0.837 (95% confidence interval [CI]: 0.792–0.881); in the validation set this value was 0.814 (95% CI: 0.744–0.884). Calibration plots and decision analysis curves revealed good calibration and clinical application value.ConclusionsWe successfully developed a model for predicting lymph node metastasis in patients with ovarian cancer, based on ultrasound examination results and clinical data. Our model accurately identified patients at high risk of lymph node metastasis and may guide the selection of appropriate treatment strategies. This model has the potential to significantly enhance the precision and efficacy of clinical management in patients with ovarian cancer.

MRI and PET/CT in the assessment of lymph node metastases in head and neck cancer

The aim of this study is to present the diagnostic accuracy of MRI and PET/CT in the evaluation of cervical lymph nodes in patients with head and neck cancer (HNC). Data of 114 patients who underwent MRI and PET/CT prior to surgery in the time period between January 2010 and September 2021 in our center is analyzed retrospectively. Histopathological results of surgical preparations serve as the gold standard. The mean time from MRI to surgery is 22.9 (± 18.7) days, and from PET/CT to surgery 21.7 (± 19.9) days. Sensitivities of 80.4% and 80.4%, specificities of 85.7% and 87.3%, PPVs of 82.0% and 83.7% and NPVs of 84.4% and 84.6% are registered for MRI and PET/CT, respectively. 37 false results are further analyzed with respect to side and level of the affected lymph node, as well as intersections of the two imaging modalities. In 29 patients (25.4%), additional findings are described in PET/CT, 7 (6.1%) of which were histologically confirmed to be further malignancies. A combination of both MRI and PET/CT imaging modalities could improve diagnostic accuracy, especially with regard to sensitivity. A notable number of additional findings in whole body acquisition leads to the potential diagnosis of further malignancies.

Prognostic value of 18F-FDG PET in uterine cervical cancer patients with stage IIICr allocated by imaging

The effect on survival of radiographic lymph node metastasis in uterine cervical cancer patients is more important than before, even though its prognostic value not been well investigated. The aim of our study is to evaluate the prognostic potential of 18F-fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) compared with Computed Tomography (CT) in uterine cervical cancer patients with stage IIICr allocated by imaging. Fifty-five patients with biopsy-proven primary cervical cancer underwent definitive radiation therapy for stages IIB–IVB of The International Federation of Gynecology and Obstetrics (FIGO) 2018 classifications. The prognostic performance of pretreatment 18F-FDG PET and CT for assessing lymph node metastasis was evaluated by two experienced readers. The PET and CT findings were correlated with the risk of progression-free survival (PFS) and overall survival (OS). Kaplan–Meier survival curves showed that PFS was significantly worse in patients with positive lymph nodes on 18F-FDG PET than in those patients with negative lymph nodes on 18F-FDG PET (p = 0.003), whereas there was no significant difference in PFS between patients with lymph nodes sized ≥ 1 cm and those sized < 1 cm (p = 0.140). Univariate analysis showed that positive lymph nodes on 18F-FDG PET was significantly associated with poor PFS (p = 0.006), whereas lymph node size was not significantly associated with poor PFS (p = 0.145). In multivariate analysis, positive lymph nodes on 18F-FDG PET was significantly associated with poor PFS (p = 0.006) and was an independent prognostic factor for PFS. 18F-FDG PET offers high prognostic value for patients with stage IIICr allocated by imaging compared with CT, suggesting that 18F-FDG PET might be useful in clinical staging decisions and thus promote optimal diagnostic and therapeutic strategies.

Diagnostic value of contrast-enhanced ultrasonography in the preoperative evaluation of lymph node metastasis in papillary thyroid carcinoma: a single-center retrospective study

BackgroundContrast-enhanced ultrasound (CEUS) has been recently used for the assessment of cervical lymph node metastasis (LNM) to guide surgical operation in patients with papillary thyroid carcinoma (PTC). However, the specificity and sensitivity of CEUS reported from previous studies are not consistent. The objective of this study was to evaluate the diagnostic value of CEUS for the metastasis of cervical lymph nodes in PTC patients based on data from one regional central hospital.MethodsThe diagnostic value of CEUS in preoperative LNM of PTC patients was concluded by comparing the results of CEUS on lymph node status with postoperative pathology examination. In addition, this study conducted hierarchical analysis of PTC patients to explore whether tumor size, different lymph node regions, and Hashimoto’s thyroiditis influence the assessment of CEUS.ResultsThis research study ultimately enrolled 965 PTC patients, including 266 males and 699 females with a mean age of 42.27 ± 11.34 years. A total of 527 patients were considered clinical-node negative, and 438 were clinical-node positive before surgery. The specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of CEUS in the assessment of LNM in PTC patients were 56.00%, 71.00%, 57.06%, 69.76% and 62.59%, respectively. For central and lateral lymph nodes, the accuracy of CEUS in PTC patients was 49.43% and 54.30%, respectively. In addition, it was shown that the accuracy of CEUS in PTC patients with Hashimoto’s thyroiditis (HT) slightly decreased to 58.44%, and the accuracy of CEUS in PTC patients with non-HT in turn increased to 64.17%. The accuracy of CEUS in non-papillary thyroid microcarcinoma (PTMC) and PTMC patients was 65.68% and 61.24%, respectively. The accuracy of CEUS in predicting central LNM was significantly different between PTC patients with or without HT (P < 0.001) in this study but not for lateral lymph nodes (P = 0.114).ConclusionThe accuracy of CEUS in the assessment of LNM in PTC is not consistently satisfactory, especially for central lymph nodes, small tumor diameters, or patients with HT. More diagnostic technologies for abnormal lymph nodes should be considered in PTC patients.

Expression of laminin332 γ2 at the invasive front is associated with tumor budding and poor prognosis in cutaneous squamous cell carcinoma.

Laminin332 is a glycoprotein consisting of α3/β3/γ2 chains, of which the γ2 chain (Ln-γ2) is expressed in tumor cells at the invasive front in many types of malignant tumors. We have previously reported that Ln-γ2 is associated with tumor invasion of cutaneous squamous cell carcinoma (cSCC) in vivo and in vitro. Recently, tumor budding (TB; invasion patterns in small clusters of less than five cancer cells in the stroma at the invasive front) has been reported to be a risk factor for lymph node metastasis in cSCC. Based on these findings, we speculated that expression of Ln-γ2 is related to TB in cSCC and would be an invasive factor that causes lymph node metastasis. In this study, we investigated the relationship between Ln-γ2 expression and clinicopathological findings, including TB, in 102 cases of cSCC using immunohistochemistry. The results showed that high expression of Ln-γ2 at the invasive front correlated with a high TB score. In addition, high Ln-γ2 expression at the invasive front was also associated with lymphatic invasion, lymph node metastasis, and poor prognosis (death or recurrence), as in TB. Furthermore, we showed a positive association between Ln-γ2 expression at the invasive front and Yes-associated protein (YAP) expression in the Hippo pathway. Our results suggest that Ln-γ2 expression at the invasive front may have a role in TB formation via YAP and contribute to prognosis by causing lymphatic invasion and lymph node metastasis. The expression of Ln-γ2 would be useful for risk assessment of lymph node metastasis and poor prognosis in routine practice of cSCC.

Investigation of vertical margin involvement in endoscopic resection for T1 colorectal cancer.

The resection of vertical margin-negative submucosally invasive colorectal cancer (CRC) relies on the pathological risk assessment of lymph node metastasis. However, no large-scale study has clarified the endoscopic resection (ER) outcome for submucosally invasive CRC, focusing on the vertical margin status. This retrospective study aimed to examine vertical margin involvement in ER for submucosally invasive CRC and explore the treatment consequences associated with vertical margin status. We analyzed 395 submucosally invasive CRC cases in 389 patients who underwent ER at our hospital between 2008 and 2020. The presence of residual tumors and simultaneous lymph node metastasis in patients who underwent additional surgery was assessed and compared between the vertical incomplete ER and the vertical margin-negative groups. Among the patients, 270 were men, with a median age of 69 years. The vertical incomplete ER rate was 21.5%, with positive vertical margins and unclear vertical margins identified in 12.2% and 9.3% of the cases, respectively. Among 154 patients who underwent additional surgery after ER, the vertical incomplete ER group had a significantly higher residual tumor rate than the vertical margin-negative group (P = 0.001). The vertical incomplete ER group had a significantly higher lymph node metastasis rate than the vertical margin-negative group (P = 0.029). This study clarified the substantial risk of vertical incomplete ER in submucosally invasive CRC and revealed the high risk of residual tumor and lymph node metastasis in vertical incomplete ER for submucosal CRC.

Invasion depth estimation of carcinoma cells using adaptive stain normalization to improve epidermis segmentation accuracy.

Submucosal invasion depth is a significant prognostic factor when assessing lymph node metastasis and cancer itself to plan proper treatment for the patient. Conventionally, oncologists measure the invasion depth by hand which is a laborious, subjective, and time-consuming process. The manual pathological examination by measuring accurate carcinoma cell invasion with considerable inter-observer and intra-observer variations is still challenging. The increasing use of medical imaging and artificial intelligence reveals a significant role in clinical medicine and pathology. In this paper, we propose an approach to study invasive behavior and measure the invasion depth of carcinoma from stained histopathology images. Specifically, our model includes adaptive stain normalization, color decomposition, and morphological reconstruction with adaptive thresholding to separate the epithelium with blue ratio image. Our method splits the image into multiple non-overlapping meaningful segments and successfully finds the homogeneous segments to measure accurate invasion depth. The invasion depths are measured from the inner epithelium edge to outermost pixels of the deepest part of particles in image. We conduct our experiments on skin melanoma tissue samples as well as on organotypic invasion model utilizing myoma tissue and oral squamous cell carcinoma. The performance is experimentally compared to three closely related reference methods and our method provides a superior result in measuring invasion depth. This computational technique will be beneficial for the segmentation of epithelium and other particles for the development of novel computer-aided diagnostic tools in biobank applications.

Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images

BackgroundLymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.MethodsA total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.ResultsOf these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740–0.750), 0.900 (95%CI: 0.807–0.993), and 0.778 (95%CI: 0.667–0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692–0.704), 0.632 (95%CI: 0.627–0.637), and 0.745 (95%CI: 0.740–0.750), respectively.ConclusionThe MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.