Near-infrared fluorescence-assisted superficial inguinal lymph-node excision for low-risk penile cancer.

OBJECTIVE: Identification of superficial inguinal lymph nodes during low-risk penile cancer surgery using near-infrared (NIR) fluorescence to improve the accuracy of lymph-node dissection and reduce the incidence of missed micrometastases and complications. METHODS: Thirty-two cases were selected, which were under the criteria of < T1, and no lymph-node metastasis was found with magnetic resonance imaging (MRI) detection. Two groups were randomly divided based on the fluorescence technique, the indocyanine green (ICG) group and the non-ICG group. In the ICG group, the ICG preparation was subcutaneously injected into the edge of the penile tumor 10 min before surgery, and the near-infrared fluorescence imager was used for observation. After the lymph nodes were visualized, the superficial inguinal lymph nodes were removed first, and then, the penis surgery was performed. The non-ICG group underwent superficial inguinal lymph-node dissection and penile surgery. RESULTS: Among the 16 patients in the ICG group, we obtained 11 lymph-node specimens using grayscale values of images (4.13 ± 0.72 vs. 3.00 ± 0.82 P = 0.003) along with shorter postoperative healing time (7.31 ± 1.08 vs. 8.88 ± 2.43 P = 0.025), and less lymphatic leakage (0 vs. 5 P = 0.04) than the 16 patients in the non-ICG group. Out of 11, 3 lymph nodes that are excised were further grouped into fluorescent and non-fluorescent regions (G1/G2) and found to be metastasized. CONCLUSION: Near-infrared fluorescence-assisted superficial inguinal lymph-node dissection in penile carcinoma is accurate and effective, and could reduce surgical complications.

Distinguishing Kikuchi-Fujimoto disease from lymphoma in patients by clinical and PET/CT features.

To develop a scheme for distinguishing Kikuchi-Fujimoto disease (KFD) from lymphoma in patients presenting enlarged lymph nodes (LNs) predominantly on the upper side of the diaphragm. From November 2015 to August 2023, 32 KFD patients and 38 lymphoma patients were pathologically confirmed and enrolled in this retrospectively study. Clinical and 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) features were collected. When comparing those PET/CT parameters, we set 5 models with different research objects: (1) all affected LNs; (2) the 5 largest affected LNs in terms of maximum diameter; (3) the 5 largest affected LNs in terms of maximum standard uptake values (SUVmax); (4) the largest affected LNs in terms of maximum diameter; (5) the largest affected LNs in terms of SUVmax. Compared to lymphoma patients, KFD patients were younger; and with higher incidence of fever, arthralgia, abnormal serum white blood cell, lactate dehydrogenase (LDH) and splenomegaly; lower incidence of affected LNs perinodal infiltration, necrosis and conglomeration; more affected LNs in Head and Neck nodes (particularly in level II) and Axillary in KFD (P ˂ .05). PET/CT parameters presented as various difference in each model. Finally, 11 clinical and PET/CT features (age ≤ 34, with fever, arthralgia, abnormal white blood cell, abnormal LDH, and without node necrosis and node conglomeration have a score of 2 each; splenomegaly, perinodal infiltration, median maximum diameter ≤ 20.5 and median SUVmax ≤ 7.1 of affected LNs in model 2 have score of 1 each) were selected as scheme items for distinguishing KFD from lymphoma. Individuals who have a total score > 8, meet the criteria for KFD. Sensitivity and specificity were high: 86.8% (95% CI: 71.9%, 95.5%) and 96.9% (95% CI: 83.7%, 99.5%), AUC = 0.975 (95% CI: 90.5%, 99.6%), respectively. It can effectively distinguish KFD from lymphoma by clinical and PET/CT parameters.

Predicting central cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: a practical nomogram based on retrospective study.

Background: In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. Methods: Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis. Results: A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively. Conclusions: The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.

The Diagnostic Performance of Magnetic Resonance Imaging in the Categorization of Pediatric Neck Lymph Nodes: Radiologic and Pathologic Correlations.

BACKGROUND/AIM: To present MRI features of neck lymph nodes in benign and malignant conditions in the pediatric population. MATERIALS AND METHODS: MRIs of the neck of 51 patients 1 to 18 years old (40 boys, 11 girls [10.08±4.73]) with lymph node biopsy were retrospectively analyzed. Those were grouped as benign including reactive (27 [52.9%]) and lymphadenitis (11 [21.6%]), and malignant (13 [25.5%]). The groups were evaluated multiparametrically in terms of quantitative and qualitative variables. RESULTS: The long axis, short axis, area, and apparent diffusion coefficient (ADC) values of the largest lymph node were 21 (17 to 24) mm, 14 (12 to 18) mm, 228.60 (144.79 to 351.82) mm 2 , 2531 (2457 to 2714) mm 2 /s for reactive, 24 (19 to 27) mm, 15 (11 to 20) mm, 271.80 (231.43 to 412.20) mm 2 , 2534 (2425 to 2594) mm 2 /s for lymphadenitis, 27 (23.50 to 31.50) mm, 20 (15 to 22) mm, 377.08 (260.47 to 530.94) mm 2 , 2337 (2254 to 2466) mm 2 /s for malignant, respectively. Statistical analysis of our data suggests that the following parameters are associated with a higher likelihood of malignancy: long axis >22 mm, short axis >16 mm, area >319 cm 2 , ADC value <2367 mm 2 /s, and supraclavicular location. Perinodal and nodal heterogeneity, posterior cervical triangle location are common in lymphadenitis ( P <0.001). Reactive lymph nodes are distributed symmetrically in both neck halves ( P <0.001). CONCLUSION: In the MRI-based approach to lymph nodes, not only long axis, short axis, surface area, and ADC, but also location, distribution, perinodal, and nodal heterogeneity should be used.

Radiomics Features Using Dual-energy CT for Lymph Nodes After Preoperative Chemotherapy for Esophageal Cancer.

BACKGROUND/AIM: Progress has been made in a triplet preoperative chemotherapy regimen for advanced esophageal cancer. We performed a preliminary investigation of the radiomics features of pathological lymph node metastasis after neoadjuvant chemotherapy using dual-energy computed tomography (DECT). PATIENTS AND METHODS: From January to December 2022, 36 lymph nodes from 10 patients with advanced esophageal cancer who underwent contrast-enhanced DECT after neoadjuvant chemotherapy and radical surgery in our department were studied. Radiomics features were extracted from iodine-based material decomposition images at the portal venous phase constructed by DECT using MATLAB analysis software. Receiver operating characteristic (ROC) analysis and cut-off values were determined for the presence or absence of pathological metastasis. RESULTS: ROC for the short axis of the pathologically positive lymph nodes showed an AUC of 0.713. Long run emphasis (LRE) within gray-level run-length matrix (GLRLM) was confirmed with a high AUC of 0.812. Sensitivity and specificity for lymph nodes with a short axis >10 mm were 0.222 and 1, respectively. Sensitivity and specificity for LRE within GLRLM were 0.722 and 0.833, respectively. Sensitivity and specificity for small zone emphasis (SZE) within gray-level size zone matrix (GLSZM) were 0.889 and 0.667, and zone percentage (ZP) values within GLSZM were 0.722 and 0.778, respectively. Discrimination of existing metastases using radiomics showed significantly higher sensitivity compared to lymph node short axis >10 mm (odds ratios of LRE, SZE, and ZP: 9.1, 28, and 9.1, respectively). CONCLUSION: Evaluation of radiomics analysis using DECT may enable a more detailed evaluation of lymph node metastasis after neoadjuvant chemotherapy.

Use of axillary ultrasound to guide breast cancer management in the genomic assay era.

INTRODUCTION: Chemotherapy is conventionally offered to non-stage IV breast cancer patients with metastatic nodes. However, the RxPONDER trial showed that chemotherapy can be omitted in selected patients with 1-3 metastatic nodes if the 21-gene assay recurrence score is ≤25. We aimed to investigate if axillary ultrasound can identify this group of patients with limited nodal burden so that they can undergo upfront surgery followed by gene assay testing, to potentially avoid chemotherapy. METHODS: T1-3, node positive, hormone receptor-positive and HER2-negative breast cancer patients ≥50 years old with axillary lymph node dissection (ALND) were reviewed from 2 centres. Patients with neoadjuvant chemotherapy and bilateral cancers were excluded. Number of ultrasound-detected abnormal axillary nodes, demographic and histological parameters were correlated with the number of metastatic nodes found on ALND. RESULTS: 138 patients were included, 59 (42.8%) and 79 (57.2%) patients had 1-3 and >3 metastatic nodes on ALND respectively. On logistic regression and ROC analysis, the number of ultrasound-detected abnormal nodes was significant (p < 0.001) for predicting limited nodal burden (ROC AUC = 0.7135). Probabilities of <4 metastatic nodes with ultrasound cut-offs of 5, 6 and 8 abnormal nodes were 0.057, 0.026 and 0.005 respectively, with 100% specificity. CONCLUSION: A cut-off of ≤5 ultrasound-detected abnormal nodes can distinguish between patients with limited versus high nodal burden, with high specificity. Hence, incorporating the number of abnormal ultrasound-detected nodes into clinical practice may prove useful in guiding between upfront surgery and gene assay testing or neoadjuvant chemotherapy in this group of patients.

Revolutionizing lymph node metastasis imaging: the role of drug delivery systems and future perspectives.

The deployment of imaging examinations has evolved into a robust approach for the diagnosis of lymph node metastasis (LNM). The advancement of technology, coupled with the introduction of innovative imaging drugs, has led to the incorporation of an increasingly diverse array of imaging techniques into clinical practice. Nonetheless, conventional methods of administering imaging agents persist in presenting certain drawbacks and side effects. The employment of controlled drug delivery systems (DDSs) as a conduit for transporting imaging agents offers a promising solution to ameliorate these limitations intrinsic to metastatic lymph node (LN) imaging, thereby augmenting diagnostic precision. Within the scope of this review, we elucidate the historical context of LN imaging and encapsulate the frequently employed DDSs in conjunction with a variety of imaging techniques, specifically for metastatic LN imaging. Moreover, we engage in a discourse on the conceptualization and practical application of fusing diagnosis and treatment by employing DDSs. Finally, we venture into prospective applications of DDSs in the realm of LNM imaging and share our perspective on the potential trajectory of DDS development.

Superior lymph node harvest by fluorescent lymphography during minimally invasive gastrectomy for gastric cancer patients with high body mass index.

BACKGROUND: Fluorescent lymphography (FL) using indocyanine green (ICG) allows for the visualization of all draining lymph nodes (LNs), thereby increasing LN retrieval. However, no studies have assessed the efficacy of FL in high body mass index (BMI) gastric cancer patients, even as LN yield decreases with increasing BMI in gastrectomy. This study aimed to investigate the influence of FL on LN retrieval in high BMI gastric cancer patients. METHODS: Gastric cancer patients who underwent laparoscopic or robotic gastrectomies from 2013 to 2021 were included. Patients were classified into two groups, with FL (FL group) or without FL (non-FL group). The effect of FL on LN retrieval was assessed by BMI. Inverse probability of treatment weighting (IPTW) was used to ensure comparability between groups. RESULTS: Retrieved LN number decreased as BMI increased regardless of FL application (P < 0.001). According to the IPTW analysis, the mean retrieved LN number was significantly higher in the FL group (48.4 ± 18.5) than in the non-FL group (39.8 ± 16.3, P < 0.001), irrespective of BMI. The FL group exhibited a significantly higher proportion of patients with 16 or more LNs (99.5%) than the non-FL group (98.1%, P < 0.001). The FL group also had a significantly higher proportion of patients with 30 or more LNs (86.6%) than the non-FL group (72.2%, P < 0.001). In both the normal and high-BMI patients, the FL group had a significantly larger percentage of patients with a higher nodal classification than the non-FL group. CONCLUSION: FL resulted in more LN retrieval, even in high BMI patients. FL ensures accurate staging by maintaining the appropriate retrieved LN number in high BMI gastric cancer patients.

Detection of abdominopelvic lymph nodes in multi-parametric MRI.

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.

Dual-energy computed tomography in a multiparametric regression model for diagnosing lymph node metastases in pancreatic ductal adenocarcinoma.

OBJECTIVE: To investigate the diagnostic value of dual-energy computed tomography (DECT) quantitative parameters in the identification of regional lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC). METHODS: This retrospective diagnostic study assessed 145 patients with pathologically confirmed pancreatic ductal adenocarcinoma from August 2016-October 2020. Quantitative parameters for targeted lymph nodes were measured using DECT, and all parameters were compared between benign and metastatic lymph nodes to determine their diagnostic value. A logistic regression model was constructed; the receiver operator characteristics curve was plotted; the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of each energy DECT parameter; and the DeLong test was used to compare AUC differences. Model evaluation was used for correlation analysis of each DECT parameter. RESULTS: Statistical differences in benign and metastatic lymph nodes were found for several parameters. Venous phase iodine density had the highest diagnostic efficacy as a single parameter, with AUC 0.949 [95% confidence interval (CI):0.915-0.972, threshold: 3.95], sensitivity 79.80%, specificity 96.00%, and accuracy 87.44%. Regression models with multiple parameters had the highest diagnostic efficacy, with AUC 0.992 (95% CI: 0.967-0.999), sensitivity 95.96%, specificity 96%, and accuracy 94.97%, which was higher than that for a single DECT parameter, and the difference was statistically significant. CONCLUSION: Among all DECT parameters for regional lymph node metastasis in PDAC, venous phase iodine density has the highest diagnostic efficacy as a single parameter, which is convenient for use in clinical settings, whereas a multiparametric regression model has higher diagnostic value compared with the single-parameter model.

A Nomogram to Predict Benign/Malignant Mediastinal Lymph Nodes Based on EBUS Sonographic Features.

Background: Endobronchial ultrasound (EBUS) sonographic features help identify benign/malignant lymph nodes while conducting transbronchial needle aspiration (TBNA). This study aims to identify risk factors for malignancy based on EBUS sonographic features and to estimate the risk of malignancy in lymph nodes by constructing a nomogram. Methods: 1082 lymph nodes from 625 patients were randomly enrolled in training (n = 760) and validation (n = 322) sets. The subgroup of EBUS-TBNA postoperative negative lymph nodes (n = 317) was randomly enrolled in a training (n = 224) set and a validation (n = 93) set. Logistic regression analysis was used to identify the EBUS features of malignant lymph nodes. A nomogram was formulated using the EBUS features in the training set and later validated in the validation set. Results: Multivariate analysis revealed that long-axis, short-axis, echogenicity, fusion, and central hilar structure (CHS) were the independent predictors of malignant lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 5 EBUS features nomogram showed good discrimination, with area under the curve values of 0.880 (sensitivity = 0.829 and specificity = 0.807) and 0.905 (sensitivity = 0.819 and specificity = 0.857). Subgroup multivariate analysis revealed that long-axis, echogenicity, and CHS were the independent predictors of malignancy outcomes of EBUS-TBNA postoperative negative lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 3 EBUS features nomogram showed good discrimination, with the area under the curve values of 0.890 (sensitivity = 0.882 and specificity = 0.786) and 0.834 (sensitivity = 0.930 and specificity = 0.636). Conclusions: Our novel scoring system based on two nomograms can be utilized to predict malignant lymph nodes.

FD-Net: Feature Distillation Network for Oral Squamous Cell Carcinoma Lymph Node Segmentation in Hyperspectral Imagery.

Oral squamous cell carcinoma (OSCC) has the characteristics of early regional lymph node metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical lymph metastases. Therefore, it is necessary to rely on a reasonable screening method to quickly judge the cervical lymph metastastic condition of OSCC patients and develop appropriate treatment plans. In this study, the widely used pathological sections with hematoxylin-eosin (H&E) staining are taken as the target, and combined with the advantages of hyperspectral imaging technology, a novel diagnostic method for identifying OSCC lymph node metastases is proposed. The method consists of a learning stage and a decision-making stage, focusing on cancer and non-cancer nuclei, gradually completing the lesions' segmentation from coarse to fine, and achieving high accuracy. In the learning stage, the proposed feature distillation-Net (FD-Net) network is developed to segment the cancerous and non-cancerous nuclei. In the decision-making stage, the segmentation results are post-processed, and the lesions are effectively distinguished based on the prior. Experimental results demonstrate that the proposed FD-Net is very competitive in the OSCC hyperspectral medical image segmentation task. The proposed FD-Net method performs best on the seven segmentation evaluation indicators: MIoU, OA, AA, SE, CSI, GDR, and DICE. Among these seven evaluation indicators, the proposed FD-Net method is 1.75%, 1.27%, 0.35%, 1.9%, 0.88%, 4.45%, and 1.98% higher than the DeepLab V3 method, which ranks second in performance, respectively. In addition, the proposed diagnosis method of OSCC lymph node metastasis can effectively assist pathologists in disease screening and reduce the workload of pathologists.

Evaluation of lymph node findings in patients with and without odontogenic infection: A clinical and ultrasonographic study / Evaluación de hallazgos ganglionares en pacientes con y sin infección odontogénica: un estudio clínico y ecográfico

Background: The present study aimed to evaluate the ultrasonographic findings of submandibular and submental lymph nodes in patients with and without odontogenic infection. Material and Methods: Systemically healthy patients aged 18-30 years old with or without odontogenic infections were included in this study. Clinical examinations were performed on all patients; those with any odontogenic infection were placed in the study group, and those without were placed in the control group. Ultrasonographic examinations of bilateral submental and submandibular lymph nodes were performed for both groups. The data were statistically analyzed using Pearson’s Chi-square test and Student’s t-test.Results: A total of 150 patients voluntarily participated (female: n=86 (57%), male: n=64 (43%)), 75 in the study group and 75 in the control group. During the ultrasonographic examination, patients in the study group had more than one lymph node the same patient was mostly detected, in the study group (right submandibular: n=42, 56%, and left submandibular: n=43, 57.3%). The long-axis diameter of the submandibular lymph nodes was 9.30±5.30 mm and 5.50±5.20 mm in the study and control groups, respectively. Conclusions: Ultrasonography revealed that the presence, number, and long-axis diameter of the submandibular lymph nodes in the patients with and without odontogenic infection were statistically different.(AU)

CD4+ CD11b+ T cells infiltrate and aggravate the traumatic brain injury depending on brain-to-cervical lymph node signaling.

AIM: We aim to identify the specific CD4+ T-cell subtype influenced by brain-to-CLN signaling and explore their role during the acute phase of traumatic brain injury (TBI). METHOD: Cervical lymphadenectomy or cervical afferent lymphatic ligation was performed before TBI. Cytokine array and western blot were used to detect cytokines, while the motor function was assessed using mNss and rotarod test. CD4+ T-cell subtypes in blood, brain, and CLNs were analyzed with Cytometry by time-of-flight analysis (CyTOF) or fluorescence-activated cell sorting (FACS). Brain edema and volume changes were measured by 9.4T MRI. Neuronal apoptosis was evaluated by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining. RESULTS: Cervical lymphadenectomy and ligation of cervical lymphatic vessels resulted in a decreased infiltration of CD4+ T cells, specifically CD11b-positive CD4+ T cells, within the affected region. The population of CD4+ CD11b+ T cells increased in ligated CLNs, accompanied by a decrease in the average fluorescence intensity of sphingosine-1-phosphate receptor-1 (S1PR1) on these cells. Administration of CD4+ CD11b+ T cells sorted from CLNs into the lateral ventricle reversed the attenuated neurologic deficits, brain edema, and lesion volume following cervical lymphadenectomy. CONCLUSION: The infiltration of CD4+ CD11b+ T cells exacerbates secondary brain damage in TBI, and this process is modulated by brain-to-CLN signaling.

Assessment of lymph node area coverage with total marrow irradiation and implementation of total marrow and lymphoid irradiation using automated deep learning-based segmentation.

BACKGROUND: Total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI) have the advantages. However, delineating target lesions according to TMI and TMLI plans is labor-intensive and time-consuming. In addition, although the delineation of target lesions between TMI and TMLI differs, the clinical distinction is not clear, and the lymph node (LN) area coverage during TMI remains uncertain. Accordingly, this study calculates the LN area coverage according to the TMI plan. Further, a deep learning-based model for delineating LN areas is trained and evaluated. METHODS: Whole-body regional LN areas were manually contoured in patients treated according to a TMI plan. The dose coverage of the delineated LN areas in the TMI plan was estimated. To train the deep learning model for automatic segmentation, additional whole-body computed tomography data were obtained from other patients. The patients and data were divided into training/validation and test groups and models were developed using the "nnU-NET" framework. The trained models were evaluated using Dice similarity coefficient (DSC), precision, recall, and Hausdorff distance 95 (HD95). The time required to contour and trim predicted results manually using the deep learning model was measured and compared. RESULTS: The dose coverage for LN areas by TMI plan had V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% median values of 46.0%, 62.1%, and 73.5%, respectively. The lowest V100% values were identified in the inguinal (14.7%), external iliac (21.8%), and para-aortic (42.8%) LNs. The median values of DSC, precision, recall, and HD95 of the trained model were 0.79, 0.83, 0.76, and 2.63, respectively. The time for manual contouring and simply modified predicted contouring were statistically significantly different. CONCLUSIONS: The dose coverage in the inguinal, external iliac, and para-aortic LN areas was suboptimal when treatment is administered according to the TMI plan. This research demonstrates that the automatic delineation of LN areas using deep learning can facilitate the implementation of TMLI.

Combining radiomics with thyroid imaging reporting and data system to predict lateral cervical lymph node metastases in medullary thyroid cancer.

BACKGROUND: Medullary Thyroid Carcinoma (MTC) is a rare type of thyroid cancer. Accurate prediction of lateral cervical lymph node metastases (LCLNM) in MTC patients can help guide surgical decisions and ensure that patients receive the most appropriate and effective surgery. To our knowledge, no studies have been published that use radiomics analysis to forecast LCLNM in MTC patients. The purpose of this study is to develop a radiomics combined with thyroid imaging reporting and data system (TI-RADS) model that can use preoperative thyroid ultrasound images to noninvasively predict the LCLNM status of MTC. METHODS: We retrospectively included 218 MTC patients who were confirmed from postoperative pathology as LCLNM negative (n=111) and positive (n=107). Ultrasound features were selected using the Student's t-test, while radiomics features are first extracted from preoperative thyroid ultrasound images, and then a two-step feature selection approach was used to select features. These features are then used to establish three regularized logistic regression models, namely the TI-RADS model (TM), the radiomics model (RM), and the radiomics-TI-RADS model (RTM), in 5-fold cross-validation to determine the likelihood of the LCLNM. The Delong's test and decision curve analysis (DCA) were used to evaluate and compare the performance of the models. RESULTS: The ultrasound features of margin and TI-RADS level, and a total of 12 selected radiomics features, were significantly different between the LCLNM negative and positive groups (p<0.05). The TM, RM, and RTM yielded an averaged AUC of 0.68±0.05, 0.78±0.06, and 0.82±0.05 in the 5-fold cross-validation dataset, respectively. RM and RTM are statistically better than TM (p<0.05 and p<0.001) according to Delong test. DCA demonstrates that RTM brings more benefit than TM and RM. CONCLUSIONS: We have developed a joint radiomics-based model for noninvasive prediction of the LCLNM in MTC patients solely using preoperative thyroid ultrasound imaging. It has the potential to be used as a complementary tool to help guide treatment decisions for this rare form of thyroid cancer.

Identificación de las características de los ganglios evaluados en el procedimiento EBUS y su relación con el diagnóstico de malignidad en instituto de alta complejidad en cáncer / Identification of the Characteristics of the Lymph Nodes Evaluated in the EBUS Procedure and their Relationship with the Diagnosis of Malignancy in a Highly Complex Cancer Institute

Introducción: La EBUS ha sido el foco de numerosos estudios destinados a evaluar su utilidad y rendimiento diagnóstico en diversas patologías. Objetivo principal: Identificación de las características ganglionares evaluadas en el procedimiento de Ultrasonido Endobronquial (EBUS) y su relación con el diagnóstico de malignidad en pacientes del Instituto Nacional del Cáncer de Colombia del 1 de enero de 2017 al 31 de marzo de 2021.Métodos: Estudio analítico observacional transversal. La recopilación de datos implicó un muestreo de casos consecutivos no probabilísticos entre individuos que cumplían los criterios de inclusión.Resultados: Un total de 75 pacientes fueron sometidos a EBUS. Se identificaron 6 características ecográficas de los ganglios de la biopsia asociadas a malignidad destacándose los ganglios mayores de 1 cm, márgenes mal definidos, ecogenicidad heterogénea, ausencia de una estructura hiliar central, presencia de signos de necrosis o coagulación y presencia de conglomerado ganglionar. Conclusión: Este estudio caracterizó la frecuencia de los hallazgos en la ultrasonografía endobronquial destacando algunas características ecográficas de los ganglios mediastínicos que podrían predecir patología maligna.

Introduction: The EBUS has been the focus of numerous studies aiming to evaluate its utility and diagnostic performance across various pathologies. Objective: Identification of the node characteristics evaluated in the Endobronchial Ultrasound (EBUS) procedure and their relationship with malignancy diagnosis in patients at the National Cancer Institute of Colombia from January 1st, 2017, to March 31st, 2021. Methods: Observational cross-sectional analytical study. Data collection involved non-probabilistic consecutive case sampling among individuals meeting the inclusion criteria.Results: A total of 75 patients underwent the EBUS procedure. Our findings revealed six predictors of malignancy based on sonographic features of biopsy nodes, including nodes larger than 1 cm, poorly defined margins, heterogeneous echogenicity, absence of a central hilar structure, presence of signs indicating necrosis or coagulation, and the presence of a ganglion conglomerate. Conclusions: This study showed that endobronchial ultrasonography has several sonographic characteristics at the time of evaluating mediastinal nodes that could predict malignant and benign pathology.

MRI morphological characteristics of lymph nodes in anal squamous cell carcinoma.

OBJECTIVES: Pre-treatment staging of anal squamous cell carcinoma (ASCC) includes pelvic MRI and [18F]-fluorodeoxyglucose positron emission tomography with computed tomography (PET-CT). MRI criteria to define lymph node metastases (LNMs) in ASCC are currently lacking. The aim of this study was to describe the morphological characteristics of lymph nodes (LNs) on MRI in ASCC patients with PET-CT-positive LNs. METHODS: ASCC patients treated at Skåne University Hospital between 2009 and 2017 were eligible for inclusion if at least one positive LN according to PET-CT and a pre-treatment MRI were present. All PET-CT-positive LNs and PET-CT-negative LNs were retrospectively identified on baseline MRI. Each LN was independently classified according to pre-determined morphological characteristics by two radiologists blinded to clinical patient information. RESULTS: Sixty-seven ASCC patients were included, with a total of 181 PET-CT-positive LNs identified on baseline MRI with a median short-axis diameter of 9.0 mm (range 7.5-12 mm). MRI morphological characteristics of PET-CT-positive LNs included regular contour (87%), round shape (89%), and homogeneous signal intensity on T2-weighed images (67%). An additional 78 PET-CT-negative LNs were identified on MRI. These 78 LNs had a median size of 6.8 mm (range 5.5-8.0 mm). The majority of PET-CT-negative LNs had a regular contour, round shape, and a homogeneous signal that was congruent to the primary tumor. CONCLUSIONS: There are MRI-specific morphological characteristics for pelvic LNs in ASCC. PET-CT-positive and negative LNs share similar morphological features apart from size, with PET-CT-positive LNs being significantly larger. Further studies are needed to determine discrimination criteria for LNM in ASCC.

Tumor and metastatic lymph nodes metabolic activity on 18F-FDG-PET/CT to predict progression-free survival in locally advanced cervical cancer.

OBJECTIVE: The present study investigated the predictive diseases progression value of preoperative fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in patients with local advanced cervical cancer (LACC). METHODS: In total, 267 patients [median age 58 (range: 27-85) years old] with LACC underwent 18F-FDG PET/CT prior to any treatment. The maximum standardized uptake values (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary lesion and metastatic lymph nodes were measured on PET/CT and correlated with clinicopathological features and progression-free survival (PFS). RESULTS: The median follow-up was 36.52 (range: 3.09-61.29) months. During the observation period, 80 (30.0%) patients exhibited disease progression. Univariate analysis showed that FIGO stage, concurrent chemoradiotherapy (CRT), serum level of carcinoembryonic antigen (CEA) and squamous cell carcinoma antigen (SCC-Ag), primary tumor MTV (pMTV) and TLG (pTLG), lymph nodes SUVmax (nSUVmax) and TLG (nTLG), and total metabolic activity (sMTV, sTLG) were associated with PFS. nSUVmax ≥ 5.29, CEA ≥ 7.11 ng/ml and deficiency of concurrent CRT were independent risk factor for PFS (p = 0.006, p = 0.008, p = 0.014). The 3-year PFS for patients with high nSUVmax were 42.2% compared to 56.3% for low nSUVmax values. CONCLUSION: Pretreatment cervical and lymph nodes metabolic parameters were associated with PFS in patients with LACC.

Development and validation of an ultrasound­based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis.

BACKGROUND: Despite advances in medical imaging technology, the accurate preoperative prediction of lymph node status remains challenging in ovarian cancer. This retrospective study aimed to investigate the feasibility of using ultrasound-based radiomics combined with preoperative clinical characteristics to predict lymph node metastasis (LNM) in patients with high-grade serous ovarian cancer (HGSOC). RESULTS: Patients with 401 HGSOC lesions from two institutions were enrolled: institution 1 for the training cohort (n = 322) and institution 2 for the external test cohort (n = 79). Radiomics features were extracted from the three preoperative ultrasound images of each lesion. During feature selection, primary screening was first performed using the sample variance F-value, followed by recursive feature elimination (RFE) to filter out the 12 most significant features for predicting LNM. The radscore derived from these 12 radiomic features and three clinical characteristics were used to construct a combined model and nomogram to predict LNM, and subsequent 10-fold cross-validation was performed. In the test phase, the three models were tested with external test cohort. The radiomics model had an area under the curve (AUC) of 0.899 (95% confidence interval [CI]: 0.864-0.933) in the training cohort and 0.855 (95%CI: 0.774-0.935) in the test cohort. The combined model showed good calibration and discrimination in the training cohort (AUC = 0.930) and test cohort (AUC = 0.881), which were superior to those of the radiomic and clinical models alone. CONCLUSIONS: The nomogram consisting of the radscore and preoperative clinical characteristics showed good diagnostic performance in predicting LNM in patients with HGSOC. It may be used as a noninvasive method for assessing the lymph node status in these patients.