Introducing the Node Reporting and Data System 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer

Ultrasound of palpable lesions: a pictorial review

Ultrasound (US) is the imaging modality of choice for evaluation of superficial palpable lesions. A large proportion of these lesions have characteristic sonographic appearance and can be confidently diagnosed with US without the need for biopsy or other intervention. The Society of Radiologists in Ultrasound (SRU) recently published a Consensus Conference Statement on superficial soft tissue masses. The goal of this manuscript is (a) to serve as a sonographic pictorial review for palpable lesions based on the SRU statement, (b) present the typical sonographic features of palpable lesions that can be confidently diagnosed with US, and (c) provide an overview of other palpable lesions with a framework to interpret the US studies and advise on appropriate further management.

The performance of the node reporting and data system 1.0 (Node-RADS) and DWI-MRI in staging patients with cervical carcinoma according to the new FIGO classification (2018)

PURPOSE

To evaluate the diagnostic accuracy of the Node-RADS score and the utility of apparent diffusion coefficient (ADC) values in predicting metastatic lymph nodes (LNs) involvement in cervical cancer (CC) patients using magnetic resonance imaging (MRI). The applicability of the Node RADS score across three readers with different years of experience in pelvic imaging was also assessed.

MATERIAL AND METHODS

Among 140 patients, 68 underwent staging MRI, neoadjuvant chemotherapy and radical surgery, forming the study cohort. Node-RADS scores of the main pelvic stations were retrospectively determined to assess LN metastatic likelihood and compared with the histological findings. Mean ADC, relative ADC (rADC), and correct ADC (cADC) values of LNs classified as Node-RADS ≥ 3 were measured and compared with histological reports, considered as gold standard.

RESULTS

Sensitivity, specificity, positive and negative predictive values (PPVs and NPVs), and accuracy were calculated for different Node-RADS thresholds. Node RADS ≥ 3 showed a sensitivity of 92.8% and specificity of 72.5%. Node RADS ≥ 4 yielded a sensitivity of 71.4% and specificity of 100%, while Node RADS 5 yielded 42.9% and 100%, respectively. The diagnostic performance of mean ADC, cADC and rADC values from 78 LNs with Node-RADS score ≥ 3 was assessed, with ADC demonstrating the highest area under the curve (AUC 0.820), compared to cADC and rADC values.

CONCLUSION

The Node-RADS score provides a standardized LNs assessment, enhancing diagnostic accuracy in CC patients. Its ease of use and high inter-observer concordance support its clinical utility. ADC measurement of LNs shows promise as an additional tool for optimizing patient diagnostic evaluation.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for regional lymph node staging of gastric cancer by CT

OBJECTIVES

Diagnostic performance of imaging for regional lymph node assessment in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. At the same time, there is an increasing demand for structured reporting using Reporting and Data Systems (RADS) to standardize oncological imaging. We aimed at investigating the diagnostic performance of Node-RADS compared to the use of various individual criteria for assessing regional lymph nodes in gastric cancer using histopathology as reference.

METHODS

In this retrospective single-center study, consecutive 91 patients (median age, 66 years, range 33-91 years, 54 men) with CT scans and histologically proven gastric adenocarcinoma were assessed using Node-RADS assigning scores from 1 to 5 for the likelihood of regional lymph node metastases. Additionally, different Node-RADS criteria as well as subcategories of altered border contour (lobulated, spiculated, indistinct) were assessed individually. Sensitivity, specificity, and Youden's index were calculated for Node-RADS scores, and all criteria investigated. Interreader agreement was calculated using Cohen's kappa.

RESULTS

Among all criteria, best performance was found for Node-RADS scores ≥ 3 and ≥ 4 with a sensitivity/specificity/Youden's index of 56.8%/90.7%/0.48 and 48.6%/98.1%/0.47, respectively, both with substantial interreader agreement (κ = 0.73 and 0.67, p < 0.01). Among individual criteria, the best performance was found for short-axis diameter of 10 mm with sensitivity/specificity/Youden's index of 56.8%/87.0%/0.44 (κ = 0.65, p < 0.01).

CONCLUSION

This study shows that structured reporting of combined size and configuration criteria of regional lymph nodes in gastric cancer slightly improves overall diagnostic performance compared to individual criteria including short-axis diameter alone. The results show an increase in specificity and unchanged sensitivity.

CLINICAL RELEVANCE STATEMENT

The results of this study suggest that Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.

KEY POINTS

• Assessment of lymph nodes in gastric cancer is still limited, and there is a lack of consensus on radiological evaluation. • Node-RADS in gastric cancer improves overall diagnostic performance compared to individual criteria including short-axis diameter. • Node-RADS may be a suitable tool for structured reporting of regional lymph nodes in gastric cancer.

Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale

The paper presents a novel approach for the automatic detection of neoplastic lesions in lymph nodes (LNs). It leverages the latest advances in machine learning (ML) with the LN Reporting and Data System (LN-RADS) scale. By integrating diverse datasets and network structures, the research investigates the effectiveness of ML algorithms in improving diagnostic accuracy and automation potential. Both Multinominal Logistic Regression (MLR)-integrated and fully connected neuron layers are included in the analysis. The methods were trained using three variants of combinations of histopathological data and LN-RADS scale labels to assess their utility. The findings demonstrate that the LN-RADS scale improves prediction accuracy. MLR integration is shown to achieve higher accuracy, while the fully connected neuron approach excels in AUC performance. All of the above suggests a possibility for significant improvement in the early detection and prognosis of cancer using AI techniques. The study underlines the importance of further exploration into combined datasets and network architectures, which could potentially lead to even greater improvements in the diagnostic process.

Diagnostic Performance of Node Reporting and Data System Magnetic Resonance Imaging Score in Detecting Metastatic Cervical Lymph Nodes of Nasopharyngeal Carcinoma

Background

The Node Reporting and Data System (Node-RADS) is a recently proposed classification system for the categorization of lymph nodes in radiological images. This study was conducted to retrospectively evaluate the diagnostic accuracy of the Node-RADS score for metastatic cervical lymph nodes on magnetic resonance imaging (MRI) of patients with nasopharyngeal carcinoma (NPC).

Methods

We retrospectively analyzed cervical lymph nodes of NPC cases. Two radiologists independently evaluated each lymph node on the MRI scans using Node-RADS. Interobserver agreement between 2 radiologists for Node-RADS score assessment was evaluated by linear weighted kappa statistics. The correlation between metastasis and the Node-RADS score of each lymph node was analyzed using multivariate regression analysis. To investigate the diagnostic performance of the Node-RADS score, we further conducted receiver operating characteristic curve analysis. Correspondently, the sensitivity, specificity, positive predictive value, and negative predictive value of each different cutoff (>1, >2, >3, and >4) were computed.

Results

In all, 119 patients with NPC were assessed, including 203 cervical lymph nodes consisting of 140 (69%) of 203 metastatic and 63 (31%) of 203 benign. The kappa agreement between the 2 readers for the Node-RADS score was 0.863 (95% CI = 0.830-0.897, P < .001). Node-RADS score on MRI scan was shown to be an independent predictive factor of lymph node metastasis after multivariate regression analysis (odds ratio [OR] = 6.745, 95% CI = 3.964-11.474, P < .001). Node-RADS achieved an area under the curve (AUC) of 0.950 (95% CI = 0.921-0.979) in diagnosing metastatic lymph nodes. When Node-RADS >2 was identified as the best cutoff based on balanced values, the sensitivity and positive predictive value were 0.92 and 0.94, respectively.

Conclusions

Our study suggests that the Node-RADS score has high accuracy in predicting NPC cervical lymph node metastasis. Nevertheless, this conclusion requires confirmation in a larger cohort of patients with NPC.

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.

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi-Parametric MRI by Deep Learning

BACKGROUND

Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi-parametric MRI (mpMRI) images.

PURPOSE

To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images.

STUDY TYPE

Retrospective.

POPULATION

Six hundred twenty-one patients with histologically proven EC from two institutions, including 111 LNM-positive and 168 LVSI-positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively.

FIELD STRENGTH/SEQUENCE

T2-weighted imaging (T2WI), contrast-enhanced T1WI (CE-T1WI), and diffusion-weighted imaging (DWI) were scanned with turbo spin-echo, gradient-echo, and two-dimensional echo-planar sequences, using either a 1.5 T or 3 T system.

ASSESSMENT

EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU-Net model for automatic segmentation. A multi-task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE-T1WI, and DWI images as inputs. The performance of the model for LNM-positive diagnosis was compared with those of three radiologists in the external test cohort.

STATISTICAL TESTS

Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant.

RESULTS

EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674).

DATA CONCLUSION

The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

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

BACKGROUND

It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection.

PURPOSE

To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer.

MATERIAL AND METHODS

This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model.

RESULTS

Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age.

CONCLUSION

The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

The role of gadolinium-based contrast agents in magnetic resonance imaging structured reporting and data systems (RADS)

Among the 28 reporting and data systems (RADS) available in the literature, we identified 15 RADS that can be used in Magnetic Resonance Imaging (MRI). Performing examinations without using gadolinium-based contrast agents (GBCA) has benefits, but GBCA administration is often required to achieve an early and accurate diagnosis. The aim of the present review is to summarize the current role of GBCA in MRI RADS. This overview suggests that GBCA are today required in most of the current RADS and are expected to be used in most MRIs performed in patients with cancer. Dynamic contrast enhancement is required for correct scores calculation in PI-RADS and VI-RADS, although scientific evidence may lead in the future to avoid the GBCA administration in these two RADS. In Bone-RADS, contrast enhancement can be required to classify an aggressive lesion. In RADS scoring on whole body-MRI datasets (MET-RADS-P, MY-RADS and ONCO-RADS), in NS-RADS and in Node-RADS, GBCA administration is optional thanks to the intrinsic high contrast resolution of MRI. Future studies are needed to evaluate the impact of the high T1 relaxivity GBCA on the assignment of RADS scores.

Performance of node reporting and data system (node-RADS): a preliminary study in cervical cancer

BACKGROUND

Node Reporting and Data System (Node-RADS) was proposed and can be applied to lymph nodes (LNs) across all anatomical sites. This study aimed to investigate the diagnostic performance of Node-RADS in cervical cancer patients.

METHODS

A total of 81 cervical cancer patients treated with radical hysterectomy and LN dissection were retrospectively enrolled. Node-RADS evaluations were performed by two radiologists on preoperative MRI scans for all patients, both at the LN level and patient level. Chi-square and Fisher's exact tests were employed to evaluate the distribution differences in size and configuration between patients with and without LN metastasis (LNM) in various regions. The receiver operating characteristic (ROC) and the area under the curve (AUC) were used to explore the diagnostic performance of the Node-RADS score for LNM.

RESULTS

The rates of LNM in the para-aortic, common iliac, internal iliac, external iliac, and inguinal regions were 7.4%, 9.3%, 19.8%, 21.0%, and 2.5%, respectively. At the patient level, as the NODE-RADS score increased, the rate of LNM also increased, with rates of 26.1%, 29.2%, 42.9%, 80.0%, and 90.9% for Node-RADS scores 1, 2, 3, 4, and 5, respectively. At the patient level, the AUCs for Node-RADS scores > 1, >2, > 3, and > 4 were 0.632, 0.752, 0.763, and 0.726, respectively. Both at the patient level and LN level, a Node-RADS score > 3 could be considered the optimal cut-off value with the best AUC and accuracy.

CONCLUSIONS

Node-RADS is effective in predicting LNM for scores 4 to 5. However, the proportions of LNM were more than 25% at the patient level for scores 1 and 2, which does not align with the expected very low and low probability of LNM for these scores.

A European Society of Oncologic Imaging (ESOI) survey on the radiological assessment of response to oncologic treatments in clinical practice

OBJECTIVES

To present the results of a survey on the assessment of treatment response with imaging in oncologic patient, in routine clinical practice. The survey was promoted by the European Society of Oncologic Imaging to gather information for the development of reporting models and recommendations.

METHODS

The survey was launched on the European Society of Oncologic Imaging website and was available for 3 weeks. It consisted of 5 sections, including 24 questions related to the following topics: demographic and professional information, methods for lesion measurement, how to deal with diminutive lesions, how to report baseline and follow-up examinations, which previous studies should be used for comparison, and role of RECIST 1.1 criteria in the daily clinical practice.

RESULTS

A total of 286 responses were received. Most responders followed the RECIST 1.1 recommendations for the measurement of target lesions and lymph nodes and for the assessment of tumor response. To assess response, 48.6% used previous and/or best response study in addition to baseline, 25.2% included the evaluation of all main time points, and 35% used as the reference only the previous study. A considerable number of responders used RECIST 1.1 criteria in daily clinical practice (41.6%) or thought that they should be always applied (60.8%).

CONCLUSION

Since standardized criteria are mainly a prerogative of clinical trials, in daily routine, reporting strategies are left to radiologists and oncologists, which may issue local and diversified recommendations. The survey emphasizes the need for more generally applicable rules for response assessment in clinical practice.

CRITICAL RELEVANCE STATEMENT

Compared to clinical trials which use specific criteria to evaluate response to oncological treatments, the free narrative report usually adopted in daily clinical practice may lack clarity and useful information, and therefore, more structured approaches are needed.

KEY POINTS

· Most radiologists consider standardized reporting strategies essential for an objective assessment of tumor response in clinical practice. · Radiologists increasingly rely on RECIST 1.1 in their daily clinical practice. · Treatment response evaluation should require a complete analysis of all imaging time points and not only of the last.

False-positive supraclavicular lymph node detected on chest computed tomography in oncology patients: Clinical implication based on subsequent neck ultrasonography and ultrasonography-guided tissue sampling

PURPOSE

The purpose of this study was to assess the prevalence and clinical implications of false-positive supraclavicular lymph node (LN) detected on chest computed tomography (CT), using subsequent neck ultrasonography (US) and US-guided tissue sampling.

METHODS

Among 172 patients with suspected supraclavicular LNs identified on CT, 87 underwent neck US or US-guided tissue sampling. Receiver operating characteristic curve and logistic regression analyses were performed to determine the diagnostic performance of US and independent predictors of false-positive LNs.

RESULTS

Among 87 patients, 49 (56.3%) were pathologically confirmed as metastases, 26 (29.9%) were negative for malignancy, and 12 (13.8%) had pseudolesions or schwannomas. The diagnostic indices were as follows: sensitivity, 91.8%; specificity, 92.3%; PPV, 95.7%; NPV, 85.7%; and accuracy, 92.0% (AUC = 0.921; 95% CI: 0.832-0.970, p < 0.001). The false-positive group had a higher mean age than the true-positive group (mean age, 69.8 ± 9.2 vs. 63.9 ± 9.8, p = 0.003). Logistic regression analyses revealed that age ≥ 65 years was the only independent predictor of false-positive LNs (OR = 4.391; 95% CI: 1.037-18.582; p = 0.044).

CONCLUSION

Subsequent US can be helpful for evaluating suspicious supraclavicular LNs detected on CT to establish appropriate management, especially in older patients.

The role of iodinated contrast media in computed tomography structured Reporting and Data Systems (RADS): a narrative review

Background and Objective

In recent years, there has been a large-scale dissemination of guidelines in radiology in the form of Reporting & Data Systems (RADS). The use of iodinated contrast media (ICM) has a fundamental role in enhancing the diagnostic capabilities of computed tomography (CT) but poses certain risks. The scope of the present review is to summarize the current role of ICM only in clinical reporting guidelines for CT that have adopted the "RADS" approach, focusing on three specific questions per each RADS: (I) what is the scope of the scoring system; (II) how is ICM used in the scoring system; (III) what is the impact of ICM enhancement on the scoring.

Methods

We analyzed the original articles for each of the latest versions of RADS that can be used in CT [PubMed articles between January, 2005 and March, 2023 in English and American College of Radiology (ACR) official website].

Key Content and Findings

We found 14 RADS suitable for use in CT out of 28 RADS described in the literature. Four RADS were validated by the ACR: Colonography-RADS (C-RADS), Liver Imaging-RADS (LI-RADS), Lung CT Screening-RADS (Lung-RADS), and Neck Imaging-RADS (NI-RADS). One RADS was validated by the ACR in collaboration with other cardiovascular scientific societies: Coronary Artery Disease-RADS 2.0 (CAD-RADS). Nine RADS were proposed by other scientific groups: Bone Tumor Imaging-RADS (BTI-RADS), Bone‑RADS, Coronary Artery Calcium Data & Reporting System (CAC-DRS), Coronavirus Disease 2019 Imaging-RADS (COVID-RADS), COVID-19-RADS (CO-RADS), Interstitial Lung Fibrosis Imaging-RADS (ILF-RADS), Lung-RADS (LU-RADS), Node-RADS, and Viral Pneumonia Imaging-RADS (VP-RADS).

Conclusions

This overview suggests that ICM is not strictly necessary for the study of bones and calcifications (CAC-DRS, BTI-RADS, Bone-RADS), lung parenchyma (Lung-RADS, LU-RADS, COVID-RADS, CO-RADS, VP-RADS and ILF-RADS), and in CT colonography (C-RADS). On the other hand, ICM plays a key role in CT angiography (CAD-RADS), in the study of liver parenchyma (LI-RADS), and in the evaluation of soft tissues and lymph nodes (NI-RADS, Node-RADS). Future studies are needed in order to evaluate the impact of the new iodinated and non-iodinate contrast media, artificial intelligence tools and dual energy CT in the assignment of RADS scores.

Applications of new radiological scores: the Node-rads in colon cancer staging

PURPOSE

The study focuses on the evaluation of the new Node Reporting and Data System 1.0 (Node-rads) scoring accuracy in the assessment of metastatic lymph nodes (LN) in patients with colon carcinoma.

MATERIAL AND METHODS

From April 2021 to May 2022, retrospective chart reviews were performed on 67 preoperative CT (Computed Tomography) of patients undergoing excisional surgery for colon cancer at the Polyclinic of Bari, Italy. Primary endpoints were to assess lymph node size and configuration to express the likelihood of a metastatic site adopting the Node-rads score system, whose categories of risk are defined from 1 (very low) to 5 (very high). The nodal postsurgical histological evaluation was the gold standard. The relationship between Node-rads score, LN size, configuration criteria (texture, border and shape) and the presence of histological metastases was statistically evaluated.

RESULTS

All surgical specimens examined had correlation with Node-rads score. They were significantly more likely to present nodes micrometastasis those patients with (a) spherical LN shape (82.8%), (b) with lymph node necrosis (100%), (c) irregular borders (87%) and (d) the LN short axis more than 10 mm (61.9%).

CONCLUSIONS

Our experience highlights how the Node-rads system proposes an intuitive and effective definition of criteria to standardize the lymph node radiological reports in colon cancer disease. Further studies are needed to streamline the classification of the nodal and peripheral LN in all the oncological imaging.

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics

BACKGROUND

Preoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision-making and prognosis.

PURPOSE

To investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease-free survival (DFS) by using machine learning methods in patients with IBC.

STUDY TYPE

Retrospective.

POPULATION

Five hundred and seventy-five women (range: 24-79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N = 386) and validation datasets (N = 189).

FIELD STRENGTH/SEQUENCE

Axial fat-suppressed T2-weighted turbo spin-echo sequence and dynamic contrast-enhanced with fat-suppressed T1-weighted three-dimensional gradient echo imaging.

ASSESSMENT

MRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best-performing of the four models to analyze the association with DFS.

STATISTICAL TESTS

Chi-squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan-Meier curve, log-rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P-value <0.05 was considered statistically significant.

RESULTS

The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22-5.80).

DATA CONCLUSION

The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Characterization of hypermetabolic lymph nodes after SARS-CoV-2 vaccination using PET-CT derived node-RADS, in patients with melanoma

This study aimed to evaluate the diagnostic accuracy of Node Reporting and Data System (Node-RADS) in discriminating between normal, reactive, and metastatic axillary LNs in patients with melanoma who underwent SARS-CoV-2 vaccination. Patients with proven melanoma who underwent a 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]-FDG PET/CT) between February and April 2021 were included in this retrospective study. Primary melanoma site, vaccination status, injection site, and 2-[18F]-FDG PET/CT were used to classify axillary LNs into normal, inflammatory, and metastatic (combined classification). An adapted Node-RADS classification (A-Node-RADS) was generated based on LN anatomical characteristics on low-dose CT images and compared to the combined classification. 108 patients were included in the study (54 vaccinated). HALNs were detected in 42 patients (32.8%), of whom 97.6% were vaccinated. 172 LNs were classified as normal, 30 as inflammatory, and 14 as metastatic using the combined classification. 152, 22, 29, 12, and 1 LNs were classified A-Node-RADS 1, 2, 3, 4, and 5, respectively. Hence, 174, 29, and 13 LNs were deemed benign, equivocal, and metastatic. The concordance between the classifications was very good (Cohen's k: 0.91, CI 0.86-0.95; p-value < 0.0001). A-Node-RADS can assist the classification of axillary LNs in melanoma patients who underwent 2-[18F]-FDG PET/CT and SARS-CoV-2 vaccination.

Prediction of regional lymph node metastasis in intrahepatic cholangiocarcinoma: it's not all about size

OBJECTIVES

Lymph node metastases (LNM) are frequent in patients with intrahepatic cholangiocarcinoma (iCC) and worsen their prognosis even after surgery. Our aim was to investigate the predictive value of lymph node (LN) short axis, the most common discriminator for identifying LNM in tumor-imaging and to develop a predictive model for regional LNM in iCC taking computed tomography (CT) features of extranodal disease into account.

MATERIALS AND METHODS

We enrolled 102 patients with pathologically proven iCC who underwent CT prior to hepatic resection and hilar lymph node dissection (LND) from 2005 to 2021. Two blinded radiologists assessed various imaging characteristics and LN diameters, which were analyzed by bivariate and multivariate logistic regression to develop a prediction model for LNM.

RESULTS

Prevalence of LNM was high (42.4 %) and estimated survival was shorter in LN-positive patients (p = 0.07). An LN short axis diameter of ≥ 9 mm demonstrated the highest predictive power for LNM. Three additional, statistically significant imaging features, presence of intrahepatic metastasis (p = 0.003), hilar tumor infiltration (p = 0.003), and tumor growth along the liver capsule (p = 0.004), were integrated into a prediction model, which substantially outperformed use of LN axis alone in ROC analysis (AUC 0.856 vs 0.701).

CONCLUSIONS

LN diameter alone proved to be a relevant but unreliable imaging-marker for LNM prediction in iCC. Our proposed prognostic model, which additionally considers intrahepatic metastases and hilar and capsular infiltration, significantly improves discriminatory power. Hilar and capsular involvement might indicate direct tumor extension to lymphatic liver structures.

MRI Evaluation of Rectal Cancer Lymph Node Staging Using Apparent Diffusion Coefficient

Introduction Colorectal cancer is the third most diagnosed cancer globally. Lymph node metastases significantly affect prognosis, emphasizing the importance of early detection and management. Despite significant advances in conventional MRI's role in staging, improvements in advanced functional imaging such as diffusion-weighted imaging (DWI) in identifying lymph node metastases persist. Objectives The aim is to evaluate the effectiveness of apparent diffusion coefficient (ADC) MRI in evaluating lymph node staging in rectal cancer. Patients and methods In a prospective study, 89 patients with stage II-III rectal cancer were grouped into two treatments: pre-operative FOLFOX4 chemotherapy and standard pre-operative chemoradiotherapy. All underwent 1.5T MRI, with T2-weighted and DWI sequences. A radiologist defined regions of interest on the tumor, lymph nodes, and intact rectal wall to calculate ADC values. Results Rectal cancer ADC's receiver operating characteristic curve had an area under the curve (AUC) of 0.688 (P < 0.001), with optimal ADC cutoff at 0.99 x 10-3 mm2/s (sensitivity: 75%, specificity: 83%). For lymph nodes, AUC was 0.508 (P < 0.001), with a cutoff of 0.9 x 10-3 mm2/s (sensitivity: 78%, specificity: 67%). No correlation between tumor and lymph node ADC values was observed. In chemotherapy patients, "healthy" inguinal lymph nodes had higher ADC values than affected ones pre-treatment (P = 0.001), a disparity fading post-treatment (P = 0.313). For chemoradiotherapy patients, the ADC difference persisted pre and post-treatment (P = 0.001). Conclusion The study of ADC-MRI showed different ADC values between tumors and lymph nodes and highlighted ADC differences between treatment groups. Notably, no correlation was observed between tumor and lymph node ADC values. However, differences were apparent when comparing "healthy" inguinal nodes with lymph nodes affected by cancer.

The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis

BACKGROUND

In clinical practice, there are currently a variety of nomograms for predicting lymph node metastasis (LNM) of prostate cancer. At the same time, some scholars have introduced machine learning (ML) into the prediction of LNM of prostate cancer. However, the predictive value of nomograms and ML remains controversial. Based on this situation, this systematic review and meta-analysis was performed to explore the predictive value of various nomograms currently recommended and newly-developed ML models for LNM in prostate cancer patients.

EVIDENCE ACQUISITION

Cochrane, PubMed, Embase, and Web of Science were searched up to November 1, 2022. The risk of bias in the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). The concordance index (C-index), sensitivity, and specificity were adopted to evaluate the predictive accuracy of the models.

RESULTS

Thirty-one studies (18,803 patients) were included. Seven kinds of nomograms currently recommended, dominated by Briganti nomogram or MSKCC nomogram, were covered in the included studies. For newly-developed ML models, the C-index for LNM prediction in the training set and validation set was 0.846 [95%CI (0.818, 0.873)] and 0.862 [95%CI (0.819-0.905)] respectively. Most ML models in the training set were based on Logistic Regression (LR), which had a sensitivity of 0.78 [95%CI (0.70, 0.85)] and a specificity of 0.85 [95%CI (0.77, 0.90)] in the training set, and a sensitivity of 0.81 [95%CI (0.67, 0.89)] and a specificity of 0.82 [95%CI (0.75, 0.88)] in the validation set. For the recommended nomograms, the C-index in the validation set was 0.745 [95%CI (0.701, 0.790)] for the Briganti nomogram and 0.714 [95%CI (0.662, 0.765)] for the MSKCC nomogram.

CONCLUSION

The predictive accuracy of ML is superior to existing clinically recommended nomograms, and appropriate updates can be conducted to existing nomograms according to special situations.

Efficacy of Local Treatment in Lymph Node Metastasis from Hepatocellular Carcinoma

Introduction

We aimed to investigate the significance of lymph node metastasis from hepatocellular carcinoma and the efficacy of local treatment.

Methods

We included patients diagnosed hepatocellular carcinoma with lymph node metastasis. The pattern of lymph node metastasis was evaluated based on imaging examinations and stratified by three locations: regional (group A), beyond regional intra-abdomen (group B), and extra-abdomen (group C) lymph node metastasis.

Results

Among 14,474 patients, 852 (5.8%) were identified as having lymph node metastasis. Regarding the location of presentation, group A showed the highest incidence, followed by groups B and C. The 1-year overall survival of patients was 31.7%. The survival significantly differed according to the location of lymph node metastasis. The 1-year overall survival rates were 39.8%, 25.5%, and 22.2% in groups A, B, and C, respectively. All patients underwent systemic treatment, with others receiving additional local treatment. Local treatment yielded superior overall survival compared with no local treatment. After propensity score matching, local treatment was associated with improved survival. Additionally, patients were stratified based on disease status at the time of diagnosis of lymph node metastasis: lymph node alone and combined extra-nodal metastasis. The survival benefits of local treatment were observed in both groups.

Conclusions

Our findings demonstrated the clinical significance of lymph node metastasis from hepatocellular carcinoma, which was well discriminated according to location, favoring regional metastasis. In patients with hepatocellular carcinoma presenting lymph node metastasis, active application of local treatment for lymph node metastasis can improve oncologic outcomes.

Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network

BACKGROUND

In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes (LNs) on contrast-enhanced computed tomography (CECT) examinations.

METHODS

In this IRB-approved retrospective single-center study, 187 CECT examinations of the head and neck region from patients with various primary diseases were collected from our local database, and 3656 LNs (19.5 ± 14.9 LNs/CECT, mean ± standard deviation) with a short-axis diameter (SAD) ≥ 5 mm were segmented manually by expert physicians. With these data, we trained an independent fully convolutional neural network based on 3D foveal patches. Testing was performed on 30 independent CECTs with 925 segmented LNs with an SAD ≥ 5 mm.

RESULTS

In total, 4,581 LNs were segmented in 217 CECTs. The model achieved an average localization rate (LR), i.e., percentage of localized LNs/CECT, of 78.0% in the validation dataset. In the test dataset, average LR was 81.1% with a mean Dice coefficient of 0.71. For enlarged LNs with a SAD ≥ 10 mm, LR was 96.2%. In the test dataset, the false-positive rate was 2.4 LNs/CECT.

CONCLUSIONS

Our trained AI model demonstrated a good overall performance in the consistent automatic localization and 3D segmentation of physiological and metastatic cervical LNs with a SAD ≥ 5 mm on CECTs. This could aid clinical localization and automatic 3D segmentation, which can benefit clinical care and radiomics research.

RELEVANCE STATEMENT

Our AI model is a time-saving tool for 3D segmentation of cervical lymph nodes on contrast-enhanced CT scans and serves as a solid base for N staging in clinical practice and further radiomics research.

KEY POINTS

• Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research.

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.

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.

Clinical Decision Support for Axillary Lymph Node Staging in Newly Diagnosed Breast Cancer Patients Based on 18F-FDG PET/MRI and Machine Learning

In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in patients with primary breast cancer is desirable. Our purpose was to investigate whether, in newly diagnosed breast cancer patients, machine-learning prediction models based on simple assessable imaging features on MRI or PET/MRI are able to determine nodal status with performance comparable to that of experienced radiologists; whether such models can be adjusted to achieve low rates of false-negatives such that invasive procedures might potentially be omitted; and whether a clinical framework for decision support based on simple imaging features can be derived from these models. Methods: Between August 2017 and September 2020, 303 participants from 3 centers prospectively underwent dedicated whole-body ¹⁸F-FDG PET/MRI. Imaging datasets were evaluated for axillary lymph node metastases based on morphologic and metabolic features. Predictive models were developed for MRI and PET/MRI separately using random forest classifiers on data from 2 centers and were tested on data from the third center. Results: The diagnostic accuracy for MRI features was 87.5% both for radiologists and for the machine-learning algorithm. For PET/MRI, the diagnostic accuracy was 89.3% for the radiologists and 91.2% for the machine-learning algorithm, with no significant differences in diagnostic performance between radiologists and the machine-learning algorithm for MRI (P = 0.671) or PET/MRI (P = 0.683). The most important lymph node feature was tracer uptake, followed by lymph node size. With an adjusted threshold, a sensitivity of 96.2% was achieved by the random forest classifier, whereas specificity, positive predictive value, negative predictive value, and accuracy were 68.2%, 78.1%, 93.8%, and 83.3%, respectively. A decision tree based on 3 simple imaging features could be established for MRI and PET/MRI. Conclusion: Applying a high-sensitivity threshold to the random forest results might potentially avoid invasive procedures such as sentinel lymph node biopsy in 68.2% of the patients.

Performance of Node-RADS Scoring System for a Standardized Assessment of Regional Lymph Nodes in Bladder Cancer Patients

BACKGROUND

Current cross-sectional imaging modalities exhibit heterogenous diagnostic performances for the detection of a lymph node invasion (LNI) in bladder cancer (BCa) patients. Recently, the Node-RADS score was introduced to provide a standardized comprehensive evaluation of LNI, based on a five-item Likert scale accounting for both size and configuration criteria. In the current study, we hypothesized that the Node-RADS score accurately predicts the LNI and tested its diagnostic performance.

METHODS

We retrospectively reviewed BCa patients treated with radical cystectomy (RC) and bilateral extended pelvic lymph node dissection, from January 2019 to June 2022. Patients receiving preoperative systemic chemotherapy were excluded. A logistic regression analysis tested the correlation between the Node-RADS score and LNI both at patient and lymph-node level. The ROC curves and the AUC depicted the overall diagnostic performance. In addition, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for different cut-off values (>1, >2, >3, >4).

RESULTS

Overall, data from 49 patients were collected. Node-RADS assigned on CT scans images, was found to independently predict the LNI after an adjusted multivariable regression analysis, both at the patient (OR 3.36, 95%CI 1.68-9.40, p = 0.004) and lymph node (OR 5.18, 95%CI 3.39-8.64, p < 0.001) levels. Node-RADS exhibited an AUC of 0.87 and 0.91 at the patient and lymph node levels, respectively. With increasing Node-RADS cut-off values, the specificity and PPV increased from 57.1 to 97.1% and from 48.3 to 83.3%, respectively. Conversely, the sensitivity and NPV decreased from 100 to 35.7% and from 100 to 79.1%, respectively. Similar trends were recorded at the lymph node level. Potentially, Node-RADS > 2 could be considered as the best cut-off value due to balanced values at both the patient (77.1 and 78.6%, respectively) and lymph node levels (82.4 and 93.4%, respectively).

CONCLUSIONS

The current study lays the foundation for the introduction of Node-RADS for the regional lymph-node evaluation in BCa patients. Interestingly, the Node-RADS score exhibited a moderate-to-high overall accuracy for the identification of LNI, with the possibility of setting different cut-off values according to specific clinical scenarios. However, these results need to be validated on larger cohorts before drawing definitive conclusions.

CT texture analysis and node-RADS CT score of mediastinal lymph nodes - diagnostic performance in lung cancer patients

BACKGROUND

Texture analysis derived from computed tomography (CT) can provide clinically relevant imaging biomarkers. Node-RADS is a recently proposed classification to categorize lymph nodes in radiological images. The present study sought to investigate the diagnostic abilities of CT texture analysis and Node-RADS to discriminate benign from malignant mediastinal lymph nodes in patients with lung cancer.

METHODS

Ninety-one patients (n = 32 females, 35%) with a mean age of 64.8 ± 10.8 years were included in this retrospective study. Texture analysis was performed using the free available Mazda software. All lymph nodes were scored accordingly to the Node-RADS classification. All primary tumors and all investigated mediastinal lymph nodes were histopathologically confirmed during clinical workup.

RESULTS

In discrimination analysis, Node-RADS score showed statistically significant differences between N0 and N1-3 (p < 0.001). Multiple texture features were different between benign and malignant lymph nodes: S(1,0)AngScMom, S(1,0)SumEntrp, S(1,0)Entropy, S(0,1)SumAverg. Correlation analysis revealed positive associations between the texture features with Node-RADS score: S(4,0)Entropy (r = 0.72, p < 0.001), S(3,0) Entropy (r = 0.72, p < 0.001), S(2,2)Entropy (r = 0.72, p < 0.001).

CONCLUSIONS

Several texture features and Node-RADS derived from CT were associated with the malignancy of mediastinal lymph nodes and might therefore be helpful for discrimination purposes. Both of the two quantitative assessments could be translated and used in clinical routine.

Normal size of benign upper neck nodes on MRI: parotid, submandibular, occipital, facial, retroauricular and level IIb nodal groups

PURPOSE

Nodal size is an important imaging criterion for differentiating benign from malignant nodes in the head and neck cancer staging. This study evaluated the size of normal nodes in less well-documented nodal groups in the upper head and neck on magnetic resonance imaging (MRI).

METHODS

Analysis was performed on 289 upper head and neck MRIs of patients without head and neck cancer. The short axial diameters (SAD) of the largest node in the parotid, submandibular, occipital, facial, retroauricular and Level IIb of the upper internal jugular nodal groups were documented and compared to the commonly used threshold of ≥ 10 mm for diagnosis of a malignant node.

RESULTS

Normal nodes in the parotid, occipital, retroauricular and Level IIb groups were small with a mean SAD ranging from 3.8 to 4.4 mm, nodes in the submandibular group were larger with a mean SAD of 5.5 mm and facial nodes were not identified. A size ≥ 10 mm was found in 0.8% of submandibular nodes. Less than 10% of the other nodal group had a SAD of ≥ 6 mm and none of them had a SAD ≥ 8 mm.

CONCLUSION

To identify malignant neck nodes in these groups there is scope to reduce the size threshold of ≥ 10 mm to improve sensitivity without substantial loss of specificity.

Regional lymph node metastasis detected on preoperative CT and/or FDG-PET may predict early recurrence of pancreatic adenocarcinoma after curative resection

The objective of this study was to evaluate the role of regional lymph node (LN) metastasis detected on preoperative CT and/or ¹⁸F-fluoro-2-deoxyglucose-positron emission tomography (FDG-PET) scans in the prediction of early tumor recurrence after curative surgical resection of pancreatic ductal adenocarcinoma (PDAC). This retrospective study included 137 patients who underwent upfront surgery with R0 resection of PDAC between 2013 and 2016. Regional LN metastasis was identified using two criteria: positive findings for regional LN metastasis on either preoperative CT or FDG-PET scans (LN_OR), or on both preoperative CT and FDG-PET scans (LN_AND). A total of 55 patients had early tumor recurrence within 12 months after curative resection. Univariable and multivariable Cox proportional hazard regression analysis showed that preoperative carbohydrate antigen 19-9 (CA19-9) levels, preoperative locally advanced status, and regional LN metastasis (both LN_OR and LN_AND criteria) were significant risk factors for early recurrence. Positive LN_OR and LN_AND showed significantly poorer recurrence-free survival compared to negative regional LN metastasis groups (p = 0.048 and p = 0.020, respectively). Compared with the LN_AND criteria, the LN_OR criteria provided higher sensitivity (22.4% vs. 15.5%, p = 0.046) and a higher negative predictive value (61.9% vs. 59.8%, p = 0.046). The LN_OR definition provided more sensitive and accurate performance in diagnosing preoperative regional LN metastasis.

Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning

Background

We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to ¹⁸F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference.

Methods

This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists.

Results

All 24 modeling methods showed good and reliable discrimination for malignant/benign LNs (area under the curve 0.75−0.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit.

Conclusions

Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-022-00296-8.

Detection of distant metastases and distant second primary cancers in head and neck squamous cell carcinoma: comparison of [18F]FDG PET/MRI and [18F]FDG PET/CT

PURPOSE

This prospective study aimed to compare the diagnostic performance of [¹⁸]FDG PET/MRI and PET/CT for the detection of distant metastases and distant second primary cancers in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

A total of 103 [¹⁸F]FDG PET/MRI examinations immediately followed by PET/CT were obtained in 82 consecutive patients for staging of primary HNSCC (n = 38), suspected loco-regional recurrence/follow-up (n = 41) or unknown primary HNSCC (n = 3). Histology and follow-up > 2 years formed the standard of reference. Blinded readers evaluated the anonymized PET/MRI and PET/CT examinations separately using a 5-point Likert score. Statistical analysis included: receiver operating characteristic (ROC) analysis, jackknife alternative free-response ROC (JAFROC) and region-of-interest (ROI)-based ROC to account for data clustering and sensitivity/specificity/accuracy comparisons for a score ≥ 3.

RESULTS

Distant metastases and distant second primary cancers were present in 23/103 (22%) examinations in 16/82 (19.5%) patients, and they were more common in the post-treatment group (11/41, 27%) than in the primary HNSCC group (3/38, 8%), p = 0.039. The area under the curve (AUC) per patient/examination/lesion was 0.947 [0.927-1]/0.965 [0.917-1]/0.957 [0.928-0.987] for PET/MRI and 0.975 [0.950-1]/0.968 [0.920-1]/0.944 [0.910-0.979] for PET/CT, respectively (p > 0.05). The diagnostic performance of PET/MRI and PET/CT was similar according to JAFROC (p = 0.919) and ROI-based ROC analysis (p = 0.574). Sensitivity/specificity/accuracy for PET/MRI and PET/CT for a score ≥ 3 was 94%/88%/89% and 94%/91%/91% per patient, 96%/90%/91% and 96%/93%/93% per examination and 95%/85%/90% and 90%/86%/88% per lesion, respectively, p > 0.05.

CONCLUSIONS

In HNSCC patients, PET/MRI and PET/CT had a high and similar diagnostic performance for detecting distant metastases and distant second primary cancers.

Predictive role of node-rads score in patients with prostate cancer candidates for radical prostatectomy with extended lymph node dissection: comparative analysis with validated nomograms

BACKGROUND AND OBJECTIVES

The Reporting and Data System (RADS) have been used in the attempts to standardize the results of oncological scans in different scenarios, such as lymph nodes, adding configuration criteria to size determination. We analyze the predictive value of preoperative Node-RADS determination at imaging for pelvic lymph node (PLN) involvement in cases of prostate cancer (PC) considered for radical prostatectomy (RP) with extended lymph node dissection (eLND) and we compare it with validate predictive nomograms (MSKCC, Briganti and Gandaglia).

METHODS

150 patients with a histological diagnosis of PC (high risk or intermediate with an estimated risk for pN+ higher than 5% using the Briganti or 7% using the Gandaglia nomogram) submitted for RP with an ePLND from 2018 and 2021 were retrospectively examined. Node-RADS determination was performed in all cases using the preoperative magnetic resonance (MR), performed by a radiologist blinded for pathologic results and compared with the MSKCC, Briganti 2012, Gandaglia 2017 and Gandaglia 2019 nomograms.

RESULTS

PLN involvement at final pathology (pN+) was found in 36/150 (24.0%) of cases and the mean percentage of positive LNs in pN+ cases was 15.90 ± 13.40. The mean number of PLNs removed at RP was similar (p = 0.188) between pN0 (23.9 ± 8.0) and pN+ (25.3 ± 8.0) cases. Considering a Node RADS 4-5 positive and a Node RADS 1-2 negative, the PPV was 100% and the NPV was 79.6%. A Node RADS score 4-5 showed a lower sensitivity (0.167 versus 0.972, 1.000, 0.971, 0.960 respectively), a higher specificity (1.000 versus 0.079, 0.096, 0.138, 0.186 respectively) and a similar AUC (0.583 versus 0.591, 0.581, 0.574, 0.597 respectively) when compared to MSKCC, Briganti 2012, Gandaglia 2017 and Gandaglia 2019 nomograms.

CONCLUSIONS

Our evaluation suggests that Node RADS score, combining configuration criteria to size determination could improve specificity in terms of pathologic PLN prediction but a very low sensitivity has been also described.

A Dual-Energy CT Radiomics of the Regional Largest Short-Axis Lymph Node Can Improve the Prediction of Lymph Node Metastasis in Patients With Rectal Cancer

Objective

To explore the value of dual-energy computed tomography (DECT) radiomics of the regional largest short-axis lymph nodes for evaluating lymph node metastasis in patients with rectal cancer.

Materials and Methods

One hundred forty-one patients with rectal cancer (58 in LNM+ group, 83 in LNM- group) who underwent preoperative total abdominal DECT were divided into a training group and testing group (7:3 ratio). After post-processing DECT venous phase images, 120kVp-like images and iodine (water) images were obtained. The highest-risk lymph nodes were identified, and their long-axis and short-axis diameter and DECT quantitative parameters were measured manually by two experienced radiologists who were blind to the postoperative pathological results. Four DECT parameters were analyzed: arterial phase (AP) normalized iodine concentration, AP normalized effective atomic number, the venous phase (VP) normalized iodine concentration, and the venous phase normalized effective atomic number. The carcinoembryonic antigen (CEA) levels were recorded one week before surgery. Radiomics features of the largest lymph nodes were extracted, standardized, and reduced before modeling. Radomics signatures of 120kVp-like images (Rad-signature120kVp) and iodine map (Rad-signatureImap) were built based on Logistic Regression via Least Absolute Shrinkage and Selection Operator (LASSO).

Results

Eight hundred thirty-three features were extracted from 120kVp-like and iodine images, respectively. In testing group, the radiomics features based on 120kVp-like images showed the best diagnostic performance (AUC=0.922) compared to other predictors [CT morphological indicators (short-axis diameter (AUC=0.779, IDI=0.262) and long-axis diameter alone (AUC=0.714, IDI=0.329)), CEA alone (AUC=0.540, IDI=0.414), and normalized DECT parameters alone (AUC=0.504-0.718, IDI=0.290-0.476)](P&lt;0.05 in Delong test). Contrary, DECT iodine map-based radiomic signatures showed similar performance in predicting lymph node metastasis (AUC=0.866). The decision curve showed that the 120kVp-like-based radiomics signature has the highest net income.

Conclusion

Predictive model based on DECT and the largest short-axis diameter lymph nodes has the highest diagnostic value in predicting lymph node metastasis in patients with rectal cancer.

Patients with Positive Lymph Nodes after Radical Prostatectomy and Pelvic Lymphadenectomy—Do We Know the Proper Way of Management?

Simple Summary

Prostate cancer (PCa) is the second most frequent malignancy in the male population worldwide. Men with a nodal invasion established after radical prostatectomy with lymph node dissection are a heterogeneous group of patients who require more thorough stratification and therapy individualization, which remain uncovered by current guidelines. Considering a multitude of prognostic factors and novel diagnostic techniques, classifying patients into narrower and more specified risk groups should be a vital part of lymph node positive PCa management in the future.

Abstract

Lymph node invasion in prostate cancer is a significant prognostic factor indicating worse prognosis. While it significantly affects both survival rates and recurrence, proper management remains a controversial and unsolved issue. The thorough evaluation of risk factors associated with nodal involvement, such as lymph node density or extracapsular extension, is crucial to establish the potential expansion of the disease and to substratify patients clinically. There are multiple strategies that may be employed for patients with positive lymph nodes. Nowadays, therapeutic methods are generally based on observation, radiotherapy, and androgen deprivation therapy. However, the current guidelines are incoherent in terms of the most effective management approach. Future management strategies are expected to make use of novel diagnostic tools and therapies, such as photodynamic therapy or diagnostic imaging with prostate-specific membrane antigen. Nevertheless, this heterogeneous group of men remains a great therapeutic concern, and both the clarification of the guidelines and the optimal substratification of patients are required.

Identifying the Candidates Who Will Benefit From Extended Pelvic Lymph Node Dissection at Radical Prostatectomy Among Patients With Prostate Cancer

PURPOSE

The therapeutic effect of extended pelvic lymph node dissection (PLND) in prostate cancer (PCa) patients is still controversial. The aim of this study was to identify the PCa patients who may benefit from extended PLND based on the 2012 Briganti nomogram.

MATERIALS AND METHODS

PCa patients who underwent radical prostatectomy (RP) plus PLND between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The probability of lymph node invasion (LNI), determined using the 2012 Briganti nomogram, was used to stratify the patients. The endpoints were overall survival (OS) and cancer-specific survival (CSS). Propensity score matching (PSM) was performed to account for potential differences between patients with and without extended PLND. Univariable and multivariable Cox regression was used to analyze the association between the number of removed nodes (NRN) and survival. Kaplan-Meier analysis was performed to estimate OS and CSS. Extended PLND was defined as NRN >75th percentile.

RESULTS

A total of 27,690 patients were included in the study. NRN was not an independent predictor of OS (p = 0.564). However, in patients with probability of LNI ≥37, multivariable analyses showed that increased NRN was associated with improved OS (hazard ratio [HR] = 0.963; p = 0.002). The 5-y OS rate was significantly higher for patients with NRN ≥12 than those with NRN <12 (94.9% vs. 91.9%, respectively; p = 0.015). In the PSM cohort, among patients with probability of LNI ≥37, multivariable analyses showed that increased NRN was associated with improved OS (HR = 0.961; p = 0.004). In addition, the 5-y OS rate was significantly higher for patients with NRN ≥12 than those with NRN <12 (94.9% vs. 89.8%, respectively; p = 0.002). However, NRN was not an independent predictor of CSS in any LNI risk subgroup (all p >0.05).

CONCLUSION

Extensive PLND might be associated with improved survival in PCa patients with a high risk of LNI, which supports the use of extended PLND in highly selected PCa patients. The results need to be validated in prospective studies with long-term follow-up.

The Role of Lymph Node Dissection for Non-Metastatic Renal Cell Carcinoma: An Updated Systematic Review and Meta-Analysis

INTRODUCTION

To compare the survival benefit of nephrectomy with or without lymph node dissection (LND) for non-metastatic, especially for high-risk renal cell carcinoma (RCC) patients by investigating different survival evaluation indicators.

EVIDENCE ACQUISITION

Eligible studies were identified until September 2021, through common databases including PubMed, the Cochrane Library, Embase and China National Knowledge Infrastructure (CNKI) on RCC and LND without language restriction. Data analysis was performed through Stata software, version 16.0 (Stata Corp., College Station, TX, USA).

EVIDENCE SYNTHESIS

22 articles were included in this meta-analysis. For non-metastatic RCC, performing LND comitantly with nephrectomy did not change the overall survival (OS) of patients of all T stages [hazard ratio (HR)=1.10, 95%CI: 0.95-1.27] and also for T2+NxM0 patients (HR=0.88, 95%CI: 0.68-1.14) as well as for T3+NxM0 patients (HR=0.95, 95%CI: 0.61-1.50). At the same time, cumulative meta-analysis has shown that the survival benefit of LND has a significant declining trend since 1979. However, it is worth noting that the operation of LND presented as a risk factor for cancer specific survival (CSS) (HR=1.22, 95%CI: 1.05-1.43).

CONCLUSIONS

Latest evidence indicated that LND might not be suitable for all non-metastatic RCC patients, especially in the current situation of various non-invasive examinations for judging lymph node metastasis and adjuvant treatments. On the contrary, excess LND could damage the survival of patients.

SYSTEMATIC REVIEW REGISTRATION

This study is registered as PROSPERO CRD42021271124.

Glioblastoma Surgery Imaging-Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software.