Image-guided core needle biopsy in the diagnosis of malignant lymphoma: comparison with surgical excision biopsy

Lymph Node Staging and Treatment in Pediatric Patients With Soft Tissue Sarcomas: A Consensus Opinion From the Children's Oncology Group, European paediatric Soft Tissue Sarcoma Study Group, and the Cooperative Weichteilsarkom Studiengruppe

Accurate staging of nodal involvement in pediatric sarcoma patients is important to determine correct systemic and local therapy, with the goal to reduce subsequent recurrences. However, differences in lymph node staging strategies, definitions, and treatment protocols between the Children's Oncology Group (COG), European paediatric Soft tissue sarcoma Study Group (EpSSG), and the Cooperative Weichteilsarkom Studiengruppe (CWS) complicate comparisons. In this article, we aim to establish internationally recognized recommendations for lymph node assessment and treatment of children and adolescents diagnosed with rhabdomyosarcoma (RMS) and non-rhabdomyosarcoma soft tissue sarcoma (NRSTS) according to the Consensus Conference Standard Operating Procedure methodology.

Analysis of a cancer-care based diagnosis program for lymphoma

Rapid time to diagnosis and consultation are important parameters in cancer care for patient satisfaction and survival. Historically, most cancer centers only accepted patients with confirmed cancer diagnoses leading to diagnostic and treatment delays. We created a comprehensive Lymphoma Diagnosis Program (LDP) within the provincial cancer care service to expedite lymphoma diagnoses. The results of the first 2 years of the LDP are summarized. The LDP successfully identified patients at high likelihood of lymphoma with 70% of accepted patients having malignancy (57% lymphoma; 13% other cancers). The use of a minimum 2.0 cm short axis measure lymph node for entry into the program proved useful. The radiological core needle biopsy diagnostic approach reduced time to diagnosis compared to pre-LDP timelines. Hospitalizations during the diagnostic process were also reduced. A cancer-care based diagnostic program for lymphoma can successfully improve the time to diagnosis and consultation for lymphoma patients.

Diagnostic Performance and Safety of Ultrasound-Guided Core Needle Biopsy for Diagnosing Lymphoma: A Systematic Review and Meta-Analysis

BACKGROUND

Lymphoma arises from transformed lymphoid cells. Although surgical excision biopsy is the standard diagnostic tool for patients with lymphoma, image-guided fine-needle aspiration (FNA) or core needle biopsy (CNB) is considered an alternative diagnostic option.

OBJECTIVE

To assess the diagnostic accuracy and safety of ultrasound (US)-guided core needle biopsy (CNB) in patients with lymphoma.

METHODS

A systematic review and meta-analysis were conducted. A literature search was performed up to January 1, 2024, using the Ovid-MELIBE and EMBASE databases to identify studies focusing on US-guided CNB in lymphoma patients. Relevant outcomes, including sensitivity, specificity, and complication rates, were extracted from the included studies. The Der-Simonian-Laird random-effects model was applied to analyze the pooled data.

RESULTS

The pooled sensitivity of US-guided CNB in lymphoma patients was 94% (95% CI = 89%-96%), and the specificity was 100% (95% CI = 94%-100%). The pooled complication rate was 1% (95% CI = 0%-3%), with self-limiting complications being the most common.

CONCLUSION

US-guided CNB demonstrated high diagnostic accuracy and low complication rates in patients with lymphoma, supporting its use as an alternative diagnostic tool.

Multimodality high-frequency ultrasound in the evaluation of cervical malignant lymphoma before biopsy

OBJECTIVE

To investigate the application value of multimodality ultrasound in the evaluation of lymphoma.

METHODS

The regression models were performed to determine whether there were differences in differentiating lymphoma from benign lymph nodes. Receiver operator curves were drawn to evaluate the diagnostic performance of three ultrasound modalities.

RESULTS

Multivariate analysis showed statistically significant differences in the long to short axes ratio, visibility of the hilum, Adler grade of blood flow, cortical echo, maximum elasticity, elastic color pattern, enhancement distribution, and Area. The combination of three modalities achieved a sensitivity of 95.6%, specificity of 87.5%, accuracy of 93.5%, positive predicted value of 97.0%, and negative predicted value of 82.4%.

CONCLUSION

Multimodal ultrasound can provide valuable differential diagnosis and improve the diagnostic performance.

Pathologic Validation of Deauville Score-Based Disease on F-18 FDG PET/CT after First-Line Treatment in Patients with Lymphoma

Purpose

PET/CT-based Deauville scoring (DS) is routinely used for lymphoma response assessment. However, pathological correlation of DS is not yet precisely documented. In the present study we aimed to pathological confirm the PET/CT-based Deauville scoring (DS) in lymphoma after first-line chemotherapy.

Materials and methods

Participants undergoing PET/CT for response assessment following first-line treatment were recruited prospectively. DS ≥ 4 lesions were interpreted as PET-positive, while DS ≤ 3 as PET-negative. Participants with a PET-positive lesion or suspicious of inadequate response (DS ≤ 3) were recruited for metabolic core-needle biopsy. True-negative and benign histopathology were kept on follow-up for three months. Histopathological, clinical and imaging findings were assessed for diagnostic performance. Procedure-related complications were also noted.

Results

In all, 148/480 participants were PET-positive, and 332/480 were PET-negative. 138/148 PET-positive and 12/332 PET-negative lesions were recruited for biopsy. Biopsy was performed in 147/150 participants (PET-positive 135; PET-negative 12). Three patients with inaccessible lesions were excluded. The diagnostic yield of the procedure was 97.3% (143/147). Histology revealed lymphoma in 106 participants (including 70% of total DS-4, 100% of DS-5a and 73.9% of DS-5b lesions), with three false-negative lesions. DS ≤ 3 lesions were true-negative except one diagnosed with lymphoma (8.3%) on follow-up. Non-lymphomatous malignancies (n = 5), granulomas (n = 12), non-specific inflammation (n = 9) and no residual disease (n = 11) were diagnosed in the rest. No major procedure-related adverse event was noted.

Conclusion

A DS-5a lesion suggests residual disease; hence, a biopsy can be prevented unless Richter's transformation is suspected. DS-4 and DS-5b lesions require a biopsy before changing the treatment plan, as a certain number of participants had non-lymphomatous F-18 FDG-avid lesions.

Uses and limitations of small-volume biopsies for the diagnosis of lymphoma

Although surgical biopsy remains the gold standard for the diagnosis of lymphoma, small-volume biopsies including fine-needle aspiration and core needle biopsy are increasingly being used as a first line diagnostic tool. Small-volume biopsies are safe, rapid and cost effective; however, diagnostic utility varies by lymphoma subtype. It is important for pathologists and clinicians to recognize both the strengths and limitations of such biopsies.

Predicting T-Cell Lymphoma in Children From 18F-FDG PET-CT Imaging With Multiple Machine Learning Models

This study aimed to examine the feasibility of utilizing radiomics models derived from 18F-FDG PET/CT imaging to screen for T-cell lymphoma in children with lymphoma. All patients had undergone 18F-FDG PET/CT scans. Lesions were extracted from PET/CT and randomly divided into training and validation sets. Two different types of models were constructed as follows: features that are extracted from standardized uptake values (SUV)-associated parameters, and CT images were used to build SUV/CT-based model. Features that are derived from PET and CT images were used to build PET/CT-based model. Logistic regression (LR), linear support vector machine, support vector machine with the radial basis function kernel, neural networks, and adaptive boosting were performed as classifiers in each model. In the training sets, 77 patients, and 247 lesions were selected for building the models. In the validation sets, PET/CT-based model demonstrated better performance than that of SUV/CT-based model in the prediction of T-cell lymphoma. LR showed highest accuracy with 0.779 [0.697, 0.860], area under the receiver operating characteristic curve (AUC) with 0.863 [0.762, 0.963], and preferable goodness-of-fit in PET/CT-based model at the patient level. LR also showed best performance with accuracy of 0.838 [0.741, 0.936], AUC of 0.907 [0.839, 0.976], and preferable goodness-of-fit in PET/CT-based model at the lesion level. 18F-FDG PET/CT-based radiomics models with different machine learning classifiers were able to screen T-cell lymphoma in children with high accuracy, AUC, and preferable goodness-of-fit, providing incremental value compared with SUV-associated features.

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Development, Validation, and Comparison of 2 Ultrasound Feature-Guided Machine Learning Models to Distinguish Cervical Lymphadenopathy

ABSTRACT

The objective of this study is to develop and validate the performance of 2 ultrasound (US) feature-guided machine learning models in distinguishing cervical lymphadenopathy. We enrolled 705 patients whose US characteristics of lymph nodes were collected at our hospital. B-mode US and color Doppler US features of cervical lymph nodes in both cohorts were analyzed by 2 radiologists. The decision tree and back propagation (BP) neural network were developed by combining clinical data (age, sex, and history of tumor) and US features. The performance of the 2 models was evaluated by calculating the area under the receiver operating characteristics curve (AUC), accuracy value, precision value, recall value, and balanced F score (F1 score). The AUC of the decision tree and BP model in the modeling cohort were 0.796 (0.757, 0.835) and 0.854 (0.756, 0.952), respectively. The AUC, accuracy value, precision value, recall value, and F1 score of the decision tree in the validation cohort were all higher than those of the BP model: 0.817 (0.786, 0.848) vs 0.674 (0.601, 0.747), 0.774 (0.737, 0.811) vs 0.702 (0.629, 0.775), 0.786 (0.739, 0.833) vs 0.644 (0.568, 0.720), 0.733 (0.694, 0.772) vs 0.630 (0.542, 0.718), and 0.750 (0.705, 0.795) vs 0.627 (0.541, 0.713), respectively. The US feature-guided decision tree model was more efficient in the diagnosis of cervical lymphadenopathy than the BP model.

Core Needle Biopsy in Lymphoma Diagnosis: The Diagnostic Performance and the Role of the Multidisciplinary Approach in the Optimization of Results

To compare the diagnostic accuracy of core needle biopsies (CNBs) and surgical excisional biopsies (SEBs), samples of lymphoid proliferation from a single institution from 2013 to 2017 (N=476) were divided into groups of CNB (N=218) and SEB (N=258). The diagnostic accuracy of these samples was evaluated as a percentage of conclusive diagnosis, according to the World Health Organization Classification of Tumours of Haematopoietic and Lymphoid Tissues . The contribution of clinical data, the assessment of sample adequacy by a pathologist during the procedure, the number and size of fragments, the needle gauge, the ancillary tests, and the type of lymphoid proliferation were also examined. The diagnostic accuracy of SEB was 97.3% and CNB 91.3% ( P =0.010). Additional factors considered essential for establishing the final diagnosis in some cases were: clinical information (20.6% CNB, 7.4% SEB; P <0.001); immunohistochemistry (96.3% CNB, 91.5% SEB; P =0.024); flow cytometry (12% CNB, 6.8% SEB; P =0.165); and other complementary tests (8.2% CNB, 17.3% SEB; P =0.058). Factors that did not influence performance were the evaluation of sample adequacy during the procedure, the number and size of fragments, and the needle gauge. Increased percentage of nondiagnostic CNB was observed in T-cell lymphomas (30%), followed by classic Hodgkin lymphoma (10.6%). The main limitation of CNB was the evaluation of morphologically heterogenous diseases. CNB is useful and safe in lymphoma diagnosis provided it is carried out by a team of experienced professionals. Having an interventional radiology team engaged with pathology is an essential component to achieve adequate rates of specific diagnoses in CNB specimens.

Lymph node excisions provide more precise lymphoma diagnoses than core biopsies: a French Lymphopath network survey

According to expert guidelines, lymph node surgical excision is the standard of care for lymphoma diagnosis. However, core needle biopsy (CNB) has become widely accepted as part of the lymphoma diagnostic workup over the past decades. The aim of this study was to present the largest multicenter inventory of lymph nodes sampled either by CNB or surgical excision in patients with suspected lymphoma and to compare their diagnostic performance in routine pathologic practice. We reviewed 32 285 cases registered in the French Lymphopath network, which provides a systematic expert review of all lymphoma diagnoses in France, and evaluated the percentage of CNB and surgical excision cases accurately diagnosed according to the World Health Organization classification. Although CNB provided a definitive diagnosis in 92.3% and seemed to be a reliable method of investigation for most patients with suspected lymphoma, it remained less conclusive than surgical excision, which provided a definitive diagnosis in 98.1%. Discordance rates between referral and expert diagnoses were higher on CNB (23.1%) than on surgical excision (21.2%; P = .004), and referral pathologists provided more cases with unclassified lymphoma or equivocal lesion through CNB. In such cases, expert review improved the diagnostic workup by classifying ∼90% of cases, with higher efficacy on surgical excision (93.3%) than CNB (81.4%; P < 10-6). Moreover, diagnostic concordance for reactive lesions was higher on surgical excision than CNB (P = .009). Overall, although CNB accurately diagnoses lymphoma in most instances, it increases the risk of erroneous or nondefinitive conclusions. This large-scale survey also emphasizes the need for systematic expert review in cases of lymphoma suspicion, especially in those sampled by using CNB.

Multimodal Sonographic Appearance and Survival Outcomes of 69 Cases of Primary Thyroid Lymphoma Over 10 Years

OBJECTIVES

To investigate ultrasound appearance and the survival outcomes for patients with primary thyroid lymphoma (PTL).

METHODS

Ultrasonic images and clinical characteristics from pathologically confirmed 69 PTL patients (2008-2019) were retrospectively analyzed. The clinical characteristics, ultrasonic characters, and prognostic factors were analyzed. Survival curves were plotted using the Kaplan-Meier method. Univariate and multivariate analyses were performed.

RESULTS

Of the 69 study patients, 23 were indolent PTL and 46 were aggressive PTL. Age (>70 years old) and elevated lactate dehydrogenase levels were statistically different clinical features between aggressive and indolent PTL. From ultrasonic images, 34 cases were nodular, 11 diffuse, and 24 mixed pattern. Mixed types displayed high invasiveness (45.7%) while diffuse types displayed higher inertness (39.1%), with statistically significant differences (P = .000). Invaded thyroid capsule and increased chaotic vascularity also showed significant differences between aggressive and indolent PTL. We also observed statistical difference in overall survival rates between aggressive and indolent PTL (P = .032). Single factor K-M analyses showed that age >70 years, aggressive pathology, and Ki67 >30% were positively correlated with the risk of poor PTL survival (P < .05).

CONCLUSIONS

Multimodal ultrasound provides accurate ultrasonographic information and facilitates PTL invasiveness diagnostics for improved clinical treatment. In addition, PTL patients aged >70 years, with aggressive pathology, and Ki67 >30% were more likely to have a poor survival outcome.

Synchronous early-stage breast cancer and axillary follicular lymphoma diagnosed by core needle biopsy: A case report

Synchronous double cancers are an infrequent finding. The focus of this study was a case of diagnosed synchronous double breast cancer (BC) and axillary (Ax) follicular lymphoma (FL). The patient was a 73-year-old woman who had been visiting her local doctor for follow-up of a fibroadenoma of the left breast, and was referred to our hospital after being diagnosed with invasive ductal carcinoma (IDC) of the left breast. Ultrasonography (US) revealed enlarged Ax lymph nodes (LNs) and US-guided core needle biopsy (CNB) was performed. CNB revealed no metastasis of IDC; however, a diagnosis of FL was made. Therefore, the patient was diagnosed with synchronous double BC and Ax FL and underwent partial surgical resection of the BC and close monitoring of the FL. To the best of our knowledge, this is the first case of malignant lymphoma diagnosed by CNB of Ax LNs during preoperative BC screening. CNB allows for a shorter waiting time for the examination, and it is considered to be minimally invasive, cost-effective and non-inferior to surgical resection in terms of specimen volume. Therefore, active preoperative evaluation of Ax LNs using US-guided CNB may contribute to BC staging, and may also help diagnose synchronous cancers.

Percutaneous Image-guided Needle Biopsy of Musculoskeletal Tumors: Technical Tips

With recent advances in imaging technology, the frequency of detecting musculoskeletal lesions has also increased. Percutaneous image-guided needle biopsy is occasionally required for the diagnosis of such lesions. Moreover, in the era of personalized cancer care, chances in histopathological diagnosis and the importance of histopathological diagnosis by percutaneous needle biopsy are increasing. However, as percutaneous needle biopsy is not a common procedure for musculoskeletal lesions, careful planning and the application of adequate techniques such as hydrodissection and the trans-osseous approach are occasionally required. In this review, we have summarized the indications and techniques for percutaneous image-guided needle biopsy for musculoskeletal lesions, including lymphatic lesions.

Research on the classification of lymphoma pathological images based on deep residual neural network

BACKGROUND

Malignant lymphoma is a type of tumor that originated from the lymphohematopoietic system, with complex etiology, diverse pathological morphology, and classification. It takes a lot of time and energy for doctors to accurately determine the type of lymphoma by observing pathological images.

OBJECTIVE

At present, an automatic classification technology is urgently needed to assist doctors in analyzing the type of lymphoma.

METHODS

In this paper, by comparing the training results of the BP neural network and BP neural network optimized by genetic algorithm (GA-BP), adopts a deep residual neural network model (ResNet-50), with 374 lymphoma pathology images as the experimental data set. After preprocessing the dataset by image flipping, color transformation, and other data enhancement methods, the data set is input into the ResNet-50 network model, and finally classified by the softmax layer.

RESULTS

The training results showed that the classification accuracy was 98.63%. By comparing the classification effect of GA-BP and BP neural network, the accuracy of the network model proposed in this paper is improved.

CONCLUSIONS

The network model can provide an objective basis for doctors to diagnose lymphoma types.