Diagnostic performance of ultrasonography in pre-operative assessment of lymph nodes in patients with cervical cancer.

OBJECTIVES: To assess the diagnostic performance of ultrasonography in pre-operative assessment of lymph nodes in patients with cervical cancer, to compare the outcomes for pelvic and para-aortic regions, and to detect macrometastases and micrometastases separately. METHODS: Patients were retrospectively included if they met the following inclusion criteria: pathologically verified cervical cancer; ultrasonography performed by one of four experienced sonographers; surgical lymph node staging, at least in the pelvic region-sentinel lymph node biopsy or systematic pelvic lymphadenectomy or debulking. The final pathological examination was the reference standard. RESULTS: 390 patients met the inclusion criteria between 2009 and 2019. Pelvic node macrometastases (≥2 mm) were confirmed in 54 patients (13.8%), and micrometastases (≥0.2 mm and <2 mm) in another 21 patients (5.4%). Ultrasonography had sensitivity 72.2%, specificity 94.0%, and area under the curve (AUC) 0.831 to detect pelvic macrometastases, while sensitivity 53.3%, specificity 94.0%, and AUC 0.737 to detect both pelvic macrometastases and micrometastases (pN1). Ultrasonography failed to detect pelvic micrometastases, with sensitivity 19.2%, specificity 85.2%, and AUC 0.522. There was no significant impact of body mass index on diagnostic accuracy. Metastases in para-aortic nodes (macrometastases only) were confirmed in 16 of 71 patients who underwent para-aortic lymphadenectomy. Ultrasonography yielded sensitivity 56.3%, specificity 98.2%, and AUC 0.772 to identify para-aortic node macrometastases. CONCLUSION: Ultrasonography performed by an experienced sonographer can be considered a sufficient diagnostic tool for pre-operative assessment of lymph nodes in patients with cervical cancer, showing similar diagnostic accuracy in detection of pelvic macrometastases as reported for other imaging methods (18F-fluorodeoxyglucose positron emission tomography/CT or diffusion-weighted imaging/MRI). It had low sensitivity for detection of small-volume macrometastases (largest diameter <5 mm) and micrometastases. The accuracy of para-aortic assessment was comparable to that for pelvic lymph nodes, and assessment of the para-aortic region should be an inseparable part of the examination protocol.

Assessment of diagnostic value of indocyanine green for lymph node metastasis in laparoscopic subtotal gastrectomy with D2 lymphadenectomy for gastric cancer: a prospective single-center study.

PURPOSE: Evaluation of diagnostic value for lymph node (LN) metastasis and LN identification using indocyanine green (ICG) fluorescence in laparoscopic subtotal gastrectomy for gastric cancer. METHODS: A prospective study on 79 patients who underwent radical subtotal gastrectomy with the use of ICG-guided LN dissection. The sensitivity and specificity of the ICG fluorescence lymphography method in detecting metastatic LNs were evaluated. RESULTS: A total of 79 patients underwent surgery. The number of LNs was retrieved: 2992 LNs, of which 2392 were fluorescent (79.9%) and 600 were nonfluorescent (20.1%). The average number of LNs dissected was 37.7 ± 11.8 LNs, and the average number of fluorescent LNs was 30.3 ± 11.1; the LN metastasis rates in the total LNs and in the ICG group were 6.79% and 7.34%, respectively. The median number of retrieved LNs in patients with LN metastases (37 [IQR, 33-47]) was higher than in patients without LN metastases (36 [IQR, 27-43]), (P = .348). The median number of fluorescent LNs was significantly higher in patients with LN metastases (32 [IQR, 26-44]) than in those without LN metastases (26 [IQR, 21-36]; P < .001). The sensitivity of ICG in metastasis detection was 75.86% (22 of 29 patients), with a false-negative rate of 24.14% (7 of 29 patients). For the identification of metastatic LNs, the sensitivity of ICG was 90.7%, with the specificity of 20.8%. The negative predictive value of nonfluorescent LNs was 97%. CONCLUSION: ICG fluorescence lymphography-guided lymphadenectomy can clearly visualize the lymphatic system and the LNs alongside the tumor. The high sensitivity in detecting metastatic LNs and the high negative predictive value of a nonfluorescent LNs suggest that this is an effective method for clinically radical gastrectomy for gastric cancer.

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.

Misleading FDG Uptake in Oncology Assessment: Beyond COVID-19 Vaccination-The Role of Pneumococcal Vaccination.

ABSTRACT: Here, we report the case of a 35-year-old woman who performed PET/CT 18F-FDG as an initial workup for HER2+ right breast invasive ductal carcinoma. Examination revealed multifocal breast involvement with homolateral lymph node involvement. Contralateral axillary adenopathy and diffuse splenic and osteomedullary hypermetabolism were also observed, suggesting associated lymphoma in the absence of a recent COVID-19 vaccination. Cytopuncture was discussed and finally postponed after the patient was found to have recently received a pneumococcal vaccination.

Magnetic resonance imaging for assessment of rectal cancer nodes after chemoradiotherapy: A single center experience.

BACKGROUND: Accurate nodal restaging is becoming clinically more important in patients with locally advanced rectal cancer (LARC) with the emergence of organ-preserving treatment after a good response to neoadjuvant chemoradiotherapy (nCRT). PURPOSE: To evaluate the accuracy of MRI in identifying negative N status (ypN0 patients) in LARC after nCRT. MATERIAL AND METHODS: 191 patients with LARC underwent MRI before and 6-8 weeks after nCRT and subsequent total mesorectal excision. Short-axis diameter of mesorectal lymph nodes was evaluated on the high resolution T2-weighted images to compare MRI restaging with histopathology.. RESULTS: 146 and 45 patients had a negative N status (ypN0) and positive N status (ypN + ), respectively. On restaging MRI, the 70 % reduction in size of the largest node was associated with an area under the curve (AUC) of 0.818 to predict ypN0 stage, with a sensitivity of 93.3 % and a negative predictive value (NPV) of 95.4 %. No nodes were observed in 38 pts (37 pts ypN0 and 1 patient ypN + ), with sensitivity and NPV of nodes disappearance for ypN0 stage of 93.3 % and 92.5 % respectively. A 2.2 mm cut-off in short-axis diameter was associated with an AUC of 0.83 for the prediction of ypN0 nodal stage, with sensitivity and NPV of 79,5% and 91.1 % respectively. CONCLUSION: A reduction in size of 70 % of the largest limph-node on MRI at rectal cancer restaging has high sensitivity and NPV for prediction of ypN0 stage after nCRT. The high NPV of node disappearance and of a ≤ 2.2 mm short-axis diameter is confirmed.

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer.

OBJECTIVES: To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC). METHODS: A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA). RESULTS: A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively. CONCLUSION: The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC. CLINICAL RELEVANCE STATEMENT: A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice. KEY POINTS: • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer.

PURPOSE: To investigate whether texture analysis of primary colonic mass in preoperative abdominal computed tomography (CT) scans of patients diagnosed with colon cancer could predict tumor grade, T stage, and lymph node involvement using machine learning (ML) algorithms. MATERIALS AND METHODS: This retrospective study included 73 patients diagnosed with colon cancer. Texture features were extracted from contrast-enhanced CT images using LifeX software. First, feature reduction was performed by two radiologists through reproducibility analysis. Using the analysis of variance method, the parameters that best predicted lymph node involvement, grade, and T stage were determined. The predictive performance of these parameters was assessed using Orange software with the k-nearest neighbor (kNN), random forest, gradient boosting, and neural network models, and their area under the curve values were calculated. RESULTS: There was excellent reproducibility between the two radiologists in terms of 49 of the 58 texture parameters that were subsequently subject to further analysis. Considering all four ML algorithms, the mean AUC and accuracy ranges were 0.557-0.800 and 47-76%, respectively, for the prediction of lymph node involvement; 0.666-0.846 and 68-77%, respectively, for the prediction of grade; and 0.768-0.962 and 81-88%, respectively, for the prediction of T stage. The best performance was achieved with the random forest model in the prediction of LN involvement, the kNN model for the prediction of grade, and the gradient boosting model for the prediction of T stage. CONCLUSION: The results of this study suggest that the texture analysis of preoperative CT scans obtained for staging purposes in colon cancer can predict the presence of advanced-stage tumors, high tumor grade, and lymph node involvement with moderate specificity and sensitivity rates when evaluated using ML models.

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.

The diagnostic value of dual-layer CT in the assessment of lymph nodes in lymphoma patients with PET/CT as a reference standard.

This study aimed to evaluate the diagnostic performances of dual-layer CT (DLCT) for the identification of positive lymph nodes (LNs) in patients with lymphoma and retrospectively included 1165 LNs obtained by biopsy from 78 patients with histologically proven lymphoma, who underwent both pretreatment DLCT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). According to 18F-FDG PET/CT findings as a reference standard, cases were categorized into the LN-negative and LN-positive groups. LNs were then randomly divided at a ratio of 7:3 into the training (n = 809) and validation (n = 356) cohorts. The patients' clinical characteristics and quantitative parameters including spectral curve slope (λHU), iodine concentration (IC) on arterial phase (AP) and venous phase (VP) images were compared between the LN-negative and LN-positive groups using Chi-square test, t-test or Mann-Whitney U test for categorical variables or quantitative parameters. Multivariate logistic regression analysis with tenfold cross-validation was performed to establish the most efficient predictive model in the training cohort. The area under the curve (AUC) was used to evaluate the diagnostic value of the predictive model, and differences in AUC were determined by the DeLong test. Moreover, the predictive model was validated in the validation cohort. Repeatability analysis was performed for LNs using intraclass correlation coefficients (ICCs). In the training cohort, long diameter (LD) had the highest AUC as an independent factors compared to other parameter in differentiating LN positivity from LN negativity (p = 0.006 to p < 0.001), and the AUC of predictive model jointly involving LD and λHU-AP was significantly elevated (AUC of 0.816, p < 0.001). While the AUC of predictive model in the validation cohort was 0.786. Good to excellent repeatability was observed for all parameters (ICC > 0.75). The combination of DLCT with morphological and functional parameters may represent a potential imaging biomarker for detecting LN positivity in lymphoma.

Contribution of FDG-PET in the diagnostic assessment of cervical lymph node metastasis in Oral Cavity Squamous Cell Carcinoma (OCSCC).

INTRODUCTION: The main objective of this study was to evaluate the contribution of FDG-PET in the diagnostic assessment of cervical lymph node metastasis in Oral Cavity Squamous Cell Carcinoma (OCSCC) and to advance a diagnostic threshold value for SUVmax in carcinomatous cervical lymph node. METHODS: 47 patients with OCSCC and suspicious cervical lymph node involvement (cN+) on FDG-PET were included in this retrospective study. The primary outcome was cervical lymph node SUVmax based on histological cervical metastatic disease (« gold standard ¼). RESULTS: Among the 77 cervical lymph nodes considered suspicious on patients' FDG-PET, 50 were really metastatic on histological examination. The lymph node SUVmax with metastatic involvement on histological examination was 4.6 ± 3.9 [2.6 - 23.7] versus 3.6 ± 1.2 [2 - 7.3] without carcinomatous involvement (p = 0.004). The lymph node size was not statistically significant according to metastatic disease (p = 0.28). DISCUSSION: A cervical lymph node SUVmax value of less than 2.6 on FDG-PET would suggest non-metastatic lymph node involvement. Supra Omohyoid Neck Dissection (SOHND) could therefore be performed in OCSCC when the SUVmax of the cervical lymph node is below this value in order to reduce the surgical morbidity of dissection of the lower internal jugular chain (Level IV).

Comprehensive analysis of clinicopathologic and sonographic features in thyroid cancer with skip lymph node metastasis: establish and assessment of a prediction nomogram.

Lateral Lymph Node Metastasis (LLNM) is common in Papillary Thyroid Carcinoma (PTC) and is associated with a poor prognosis. LLNM without central lymph node metastasis as skip metastasis is not common. We aimed to investigate clinicopathologic and sonographic risk factors for skip metastasis in PTC patients, and to establish a nomogram for predicting the possibility of skip metastasis in order to determine the therapeutic strategy. We retrospectively reviewed the data of 1037 PTC patients who underwent surgery from 2016 to 2020 at a single institution. Univariate and multivariate analyses were used to identify the clinicopathologic and preoperative sonographic risk factors of skip metastasis. A nomogram including the risk factors for predicting skip metastasis was further developed and validated. The incidence of skip metastasis was 10.7%. The univariate and multivariate analyses suggested that gender (p= 0.001), tumor location (p= 0.000), extrathyroidal extension (p= 0.000), and calcification (p= 0.000) were independent risk factors. For papillary thyroid microcarcinoma, tumor location (p= 0.000) and calcification (p= 0.001) were independent risk factors. A nomogram according to the clinicopathologic and sonographic predictors was developed. The receiver operating characteristic curve indicated that AUC was 0.824 and had an excellent consistency. The calibration plot analysis showed a good performance and clinical utility of the model. Decision curve analysis revealed it was clinically useful. A nomogram for predicting the probability of skip metastasis was developed, which exhibited a favorable predictive value and consistency. For the female PTC patient, tumor located at the upper pole is more likely to have skip metastasis. Surgeons and sonographers should pay close attention to the patients who have the risk factors. Evidence level: This article's evidence level is 3. Level 3 evidence is derived from non-randomized, controlled clinical trials. In this study, patients who receive an intervention are compared to a control group. Authors may detect a statistically significant and clinically relevant outcome.

Magnetic resonance imaging in preoperative assessment of the mesorectal nodal status of patients with rectal cancer - Can it be trusted?

PURPOSE: Magnetic resonance imaging (MRI) is a fundamental diagnostic modality for the evaluation of primary rectal cancer, but MRI assessment of nodal involvement remains a confounding factor. METHOD: This prospective cohort study was conducted to investigate the accuracy of preoperative MRI in the assessment of nodal status by comparing histopathology reports to MRI findings on a node-by-node basis in 69 patients with rectal cancer. RESULTS: Primary surgery was performed in 40 (58.0%) patients; 29 (42.0%) study patients underwent neoadjuvant chemoradiotherapy (CRT). Histopathological examination revealed T1 tumour in 8 (11.6%) patients, T2 tumour in 30 (43.5%), and T3 tumour in 25 (36.2%). In total, 897 lymph nodes (LNs) have been harvested (13.1 ± 5.4 LNs per specimen). There were 77 MRI-suspicious LNs, 21 (27.3%) of which were histologically proven malignant. The sensitivity of MRI for assessing nodal involvement was 51.2% and specificity 93.4%. Of the 28 patients with MRI-suspicious LNs the diagnosis was correct in 42.8%. The MRI accuracy was 33.3% in "primary surgery" subgroup (n = 18, malignant LNs found in 6 patients). Diagnosis of MRI-negative LNs was correct in 90.2% of study patients; malignant nodes were found in 9.8% of patients initially classified as cN0. CONCLUSIONS: MRI prediction of nodal status in patients with rectal cancer has very low accuracy. Decisions regarding neoadjuvant CRT should not be based on MRI assessment of nodal status, but on the MRI evaluation of tumour depth invasion (T stage and relationship between the tumour and mesorectal fascia).

DIAGNOSTICS OF LYMPHOGENIC METASTASIS IN PATIENTS WITH RECTAL CANCER BY COMBINING MRI WITH BLOOD CEA ASSESSMENT.

AIM: To improve the diagnostics of lymphogenic metastasis in patients with rectal cancer (RCa) by combining magnetic resonance imaging (MRI) with the blood carcinoembryonic antigen (CEA) level assessment. MATERIALS AND METHODS: We have systematized and analyzed the results of the examination and treatment of 77 patients with stage II-III rectal adenocarcinoma (T2-3N0-2M0). Before the start of neoadjuvant treatment as well as 8 weeks after its completion, computed tomography (CT) and MRI were performed. We analyzed such prognostic criteria as the size, shape, and structure of lymph nodes as well as the patterns of contrast accumulation. As a prognostic marker, CEA levels in the blood of patients with RCa before surgical treatment were assessed. RESULTS: Radiological exams showed a rounded shape and heterogeneous structure to be the most informative for predicting metastatic lymph node damage, increasing the probability by 4.39 and 4.98 times, respectively. After neoadjuvant treatment, the percentage of positive histopathological reports on lymph node involvement decreased significantly to 21.6% (р ˂ 0.001). MRI showed 76% sensitivity and 48% specificity for assessing lymphogenic metastasis. CEA levels differed significantly between stages II and III (N1-2) (р ˂ 0.032) with a threshold value of 3.95 ng/ml. CONCLUSIONS: In order to increase the effectiveness of the diagnosis of lymphogenic metastasis using radiological examination methods in RCa patients, such prognostic criteria as the round shape and heterogeneous structure of the lymph nodes and the threshold level of CEA should be considered.

Accuracy of Diffusion Weighted Imaging in Assessment of Pelvic Lymphnode Metastasis in Patients with Endometrial Cancer.

OBJECTIVE: To evaluate the sensitivity and specificity of diffusion-weighted imaging in determining metastatic pelvic lymph nodes in patients of endometrial cancer and comparing its accuracy with contrast-enhanced sequence of MRI, taking histopathology as gold standard. STUDY DESIGN: Retrospective Study. Place and Duration of the Study: Department of Radiology, the Aga Khan University Hospital Karachi, from January to December 2021. METHODOLOGY: Fifty-eight adult females with biopsy proven endometrial carcinoma and complete medical records were included through convenience sampling. Patients who did not have complete medical records were excluded. Studied variables included signal characteristics of lymph nodes and their short axis diameter. The sensitivity and specificity of DWI and contrast-enhanced MRI for evaluation of diseased lymph nodes were calculated using histopathology as the gold standard. RESULTS: Among 58 patients with histopathologically proven endometrial cancer, 14 had metastatic lymphadenopathy. DWI-weighted imaging in the evaluation of metastatic and non-metastatic lymph nodes had sensitivity of 81.1% while specificity, the positive and negative predictive value of 88.8%, 72.2%, and 82.5% and on contrast-enhanced imaging as 66.6%, 58.1%, 35.7%, and 83.3%, respectively. CONCLUSION: The DWI shows better accuracy in evaluation and discrimination between metastatic and non-metastatic lymph nodes as compared to contrast-enhanced MRI examination for the evaluation of diseased lymph nodes in patients with endometrial cancer. KEY WORDS: DWI, Contrast-enhanced MRI, Lymph node, Endometrial cancer.

Pre-operative localisation of axillary lymph nodes using radiofrequency identification (RFID) tags: a feasibility assessment in 75 cases.

AIM: To evaluate the safety and feasibility of using radiofrequency identification (RFID) tags for the localisation of axillary nodes prior to targeted excision in a National Health Service (NHS) breast unit. MATERIALS AND METHODS: Retrospective data collection was carried out to analyse the first 75 cases of RFID-targeted axillary nodes inserted between 12 June 2019 and 27 October 2022, during which an overall total of 1,296 breast and axillary tags were deployed in 1,120 patients. RESULTS: Of the 75 axillary tags, 70 (93%) had a primary breast cancer and five (7%) had no known breast cancer but had an abnormal node targeted for diagnostic excision. Of the 70 with breast cancer, 20 (29%) underwent neoadjuvant chemotherapy (NAC) including one neoadjuvant endocrine therapy. Localisations were performed an average of 11 days before surgery (median 6, range 1-95; n=75). Patients undergoing NAC had their tags inserted after completing treatment due to the artefact caused by the tags on magnetic resonance imaging (MRI). Tag deployment had a 100% success rate, with 62 tags (83%) lying within the node and 13 tags (17%) lying directly adjacent to the node, either in direct contact (nine of 13), or a maximum of 8 mm from the target (four of 13). All tags and their respective nodes were excised successfully at surgery with no significant complications. There were four cases of tag dislodgement during excision, but overall, this did not compromise retrieval of the tag or the node. CONCLUSIONS: The use of RFID tags for the preoperative localisation of axillary nodes is safe and feasible.

18 F-FDG PET/CT in Extensive Follicular Dendritic Cell Sarcoma With Response Assessment to Chemotherapy.

ABSTRACT: Follicular dendritic cell sarcoma (FDCS) is a low-grade sarcoma of mesenchymal dendritic cell origin, and it constitutes <0.4% of soft tissue sarcomas. We report a rare case of FDCS in a 32-year-old man. 18 F-FDG PET/CT demonstrated the involvement of cervical, axillary, mediastinal, abdominal, and pelvic groups of lymph nodes and spleen. A cervical lymph node biopsy suggested FDCS. 18 F-FDG PET/CT scan done after 3 cycles of chemotherapy (CHOP regime) revealed a complete metabolic response. This case presents the rarity of extensive presentation and complete response to the CHOP regime.

German specialists treating testicular cancer follow different guidelines with resulting inconsistency in assessment of retroperitoneal lymph-node metastasis: clinical implications and possible corrective measures.

BACKGROUND: Testicular germ cell tumors (GCTs) are aggressive but highly curable tumors. To avoid over/undertreatment, reliable clinical staging of retroperitoneal lymph-node metastasis is necessary. Current clinical guidelines, in their different versions, lack specific recommendations on how to measure lymph-node metastasis. OBJECTIVE: We aimed to assess the practice patterns of German institutions frequently treating testicular cancer for measuring retroperitoneal lymph-node size. METHODS: An 8-item survey was distributed among German university hospitals and members of the German Testicular Cancer Study Group. RESULTS: In the group of urologists, 54.7% assessed retroperitoneal lymph nodes depending on their short-axis diameter (SAD) (33.3% in any plane, 21.4% in the axial plane), while 45.3% used long-axis diameter (LAD) for the assessment (42.9% in any plane, 2.4% in the axial plane). Moreover, the oncologists mainly assessed lymph-node size based on the SAD (71.4%). Specifically, 42.9% of oncologists assessed the SAD in any plane, while 28.5% measured this dimension in the axial plane. Only 28.6% of oncologists considered the LAD (14.3% in any plane, 14.3% in the axial plane). None of the oncologists and 11.9% of the urologists (n = 5) always performed an MRI for the initial assessment, while for follow-up imaging, the use increased to 36.5% of oncologists and 31% of urologists. Furthermore, only 17% of the urologists, and no oncologists, calculated lymph-node volume in their assessment (p = 0.224). CONCLUSION: Clear and consistent measurement instructions are urgently needed to be present in all guidelines across different specialistic fields involved in testicular cancer management.

Non-invasive Assessment of Axillary Lymph Node Metastasis Risk in Early Invasive Breast Cancer Adopting Automated Breast Volume Scanning-Based Radiomics Nomogram: A Multicenter Study.

OBJECTIVE: The aim of the work described here was to develop a non-invasive tool based on the radiomics and ultrasound features of automated breast volume scanning (ABVS), clinicopathological factors and serological indicators to evaluate axillary lymph node metastasis (ALNM) in patients with early invasive breast cancer (EIBC). METHODS: We retrospectively analyzed 179 ABVS images of patients with EIBC at a single center from January 2016 to April 2022 and divided the patients into training and validation sets (ratio 8:2). Additionally, 97 ABVS images of patients with EIBC from a second center were enrolled as the test set. The radiomics signature was established with the least absolute shrinkage and selection operator. Significant ALNM predictors were screened using univariate logistic regression analysis and further combined to construct a nomogram using the multivariate logistic regression model. The receiver operating characteristic curve assessed the nomogram's predictive performance. DISCUSSION: The constructed radiomics nomogram model, including ABVS radiomics signature, ultrasound assessment of axillary lymph node (ALN) status, convergence sign and erythrocyte distribution width (standard deviation), achieved moderate predictive performance for risk probability evaluation of ALNs in patients with EIBC. Compared with ultrasound, the nomogram model was able to provide a risk probability evaluation tool not only for the ALNs with positive ultrasound features but also for micrometastatic ALNs (generally without positive ultrasound features), which benefited from the radiomics analysis of multi-sourced data of patients with EIBC. CONCLUSION: This ABVS-based radiomics nomogram model is a pre-operative, non-invasive and visualized tool that can help clinicians choose rational diagnostic and therapeutic protocols for ALNM.