Prognostic CT and MR imaging features in patients with untreated extranodal non-Hodgkin lymphoma of the head and neck region

Nomograms Predict PFS and OS for SCLC Patients After Standardized Treatment: A Real-World Study

Purpose

This study aims to investigate the process of small cell lung cancer (SCLC) patients from achieving optimal efficacy to experiencing disease progression until death. It examines the predictive value of the treatment response on progression free survival (PFS) and overall survival (OS) of SCLC patients.

Patients and Methods

We conducted a retrospective analysis on 136 SCLC patients diagnosed from 1992 to 2018. Important prognostic factors were identified to construct nomogram models. The predictive performance of the models was evaluated using the receiver operating characteristic curves and calibration curves. Survival differences between groups were compared using Kaplan-Meier survival curves. Subsequently, an independent cohort consisting of 106 SCLC patients diagnosed from 2014 to 2021 was used for validation.

Results

We constructed two nomograms to predict first-line PFS (PFS1) and OS of SCLC. The area under the receiver operating characteristic curves for the PFS1 nomogram predicting PFS at 3-, 6-, and 12-months were 0.919 (95% CI: 0.867-0.970), 0.908 (95% CI: 0.860-0.956) and 0.878 (95% CI: 0.798-0.958), and for the OS nomogram predicting OS at 6-, 12-, and 24-months were 0.814 (95% CI: 0.736-0.892), 0.819 (95% CI: 0.749-0.889) and 0.809 (95% CI: 0.678-0.941), indicating those two models with a high discriminative ability. The calibration curves demonstrated the models had a high degree of consistency between predicted and observed values. According to the risk scores, patients were divided into high-risk and low-risk groups, showing a significant difference in survival rate. And these findings were validated in another independent validation cohort.

Conclusion

Based on the patients' treatment response after standardized treatment, we developed and validated two nomogram models to predict PFS1 and OS of SCLC. The models demonstrated good accuracy, reliability and clinical applicability by validating in an independent cohort.

Safety and Efficacy of Computed Tomography-Guided Iodine-125 Brachytherapy as a Salvage Treatment for Locoregional Lymph Node Recurrence of Esophageal Cancer

PURPOSE

To investigate the clinical safety and efficacy of computed tomography (CT)-guided iodine-125 (¹²⁵I) brachytherapy as a salvage treatment for esophageal cancer with locoregional lymph node recurrence (LNR).

MATERIALS AND METHODS

This retrospective study included patients with esophageal cancer who developed locoregional LNR after initial curative resection followed by CT-guided ¹²⁵I brachytherapy as a salvage treatment (January 2014 to January 2020). Local tumor progression-free survival (LTPFS) was assessed using Response Evaluation Criteria in Solid Tumors, v1.1. Clinical response was evaluated with the Numerical Rating Scale pain score, and adverse events were evaluated with the Common Terminology Criteria for Adverse Events (v5.0). A layered Cox proportional hazards model was used to determine independent factors affecting LTPFS.

RESULTS

A total of 52 patients (mean age, 60 years) were included in this study. The median follow-up was 9.3 months (range, 4.3-12 months). The median LTPFS was 7.0 months (interquartile range, 5.0-9.5 months). The local control rates were 100%, 94.2%, 59.6%, and 13.4% at 1, 3, 6, and 12 months, respectively. The overall survival rates were 100%, 100%, 82.6%, and 36.5% at 1, 3, 6, and 12 months, respectively. The number of locoregional LNRs (hazard ratio [HR], 2.38 [95% confidence interval {CI}, 1.11-5.10]; P = .026), clinical stage at diagnosis (HR, 8.12 [95% CI, 3.19-20.66]; P < .001), and pathologic stage (HR, 5.74 [95% CI, 2.14-15.39]; P = .001) were independent factors for LTPFS. The rate of pain relief was 96.4% (27 of 28). Treatment-related death was not observed.

CONCLUSIONS

CT-guided ¹²⁵I radioactive seed implantation resulted in pain relief and short to midterm local control.

Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes

PURPOSE

To investigate the application of deep learning combined with traditional radiomics methods for classifying enlarged cervical lymph nodes.

METHODS

The clinical and computed tomography (CT) imaging data of 276 patients with enlarged cervical lymph nodes (150 with lymph-node metastasis, 65 with lymphoma, and 61 with benign lymphadenopathy) who were treated at the hospital from January 2015 to January 2021 were retrospectively analysed. The patients were randomly divided into a training group and a test group at a ratio of 8:2. The radiomics features were extracted using one-by-one convolution and neural network activation, filtered with the least absolute shrinkage and selection operator (LASSO) model, and used to construct a discrimination model with PyTorch. Then, the performance of the model was compared with the radiologists' diagnostic performance. The neural network model was evaluated using the area under the receiver-operator characteristic curve (AUC), and the accuracy, sensitivity, and specificity were analysed.

RESULTS

A total of 102 features, comprising five traditional radiomic features and 97 deep learning features, were selected with LASSO and used to construct a discrimination model, which achieved a total accuracy of 87.50%. The AUC value, specificity, and sensitivity were, respectively, 0.92, 92.30%, and 90.00% for metastatic lymph nodes, 0.87, 95.45%, and 83.33% for benign lymphadenopathy, and 0.88, 90.47%, and 85.71% for lymphoma. The accuracies of the radiologists' diagnoses were 62.68% and 62.68%. The diagnostic performance of the model was significantly different from that of the radiologists (p < 0.05).

CONCLUSION

CT-based deep learning combined with the traditional radiomics methods has a high diagnostic value for the classification of cervical enlarged lymph nodes.

Computed Tomography Imaging Findings for Predicting Histological Subtypes and Clinical Outcomes in Patients With Head and Neck Nodal Involvement of Diffuse Large B-Cell Lymphoma and Follicular Lymphoma

OBJECTIVE

This study aimed to assess computed tomography (CT) imaging findings for predicting the histological subtypes and clinical outcomes in patients with head and neck nodal involvement of diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL).

METHODS

This retrospective study included 64 patients with histologically confirmed head and neck nodal lymphoma (43 with DLBCL and 21 with FL) who underwent pretreatment CT examinations. The CT imaging findings were retrospectively assessed and compared according to the 2 pathologies and their clinical outcomes.

RESULTS

Multiplicity (86% vs 57%, P < 0.05), necrosis (44% vs 5%, P < 0.01), ill-demarcated margin (33% vs 0%, P < 0.01), and surrounding fat stranding (56% vs 14%, P < 0.01) were significantly more frequent in DLBCLs than in FLs. Multivariate logistic regression analysis revealed that necrosis was a significant factor for predicting the diagnosis of DLBCL (P < 0.01). Multiplicity (100% vs 67%, P < 0.01), bilaterality (44% vs 13%, P < 0.05), and surrounding fat stranding (69% vs 28%, P < 0.01) were significantly more frequent in the poor outcome group than in the good outcome group. Multivariate logistic regression analysis revealed that bilaterality and surrounding fat stranding were significant factors for predicting poor outcomes (P < 0.05).

CONCLUSIONS

In patients with head and neck nodal lymphoma, necrosis was useful for predicting the diagnosis of DLBCL, whereas bilaterality and surrounding fat stranding were useful for predicting poor outcomes.

Extranodal lymphoma of the head and neck: a pictorial essay

Primary extranodal lymphoma is defined as a lymphoma at a solitary extranodal site, with or without involvement of the lymph nodes. The clinical and radiological features of extranodal lymphoma have been documented in recent studies. In this pictorial essay, we reviewed imaging findings of extranodal lymphoma in the head and neck region.

CT findings predict survival of patients with peripheral T cell lymphoma: a preliminary study

Background Peripheral T-cell lymphoma (PTCL) is an uncommon disease with poor clinical outcomes. Radiological reports on the survival of patients with PTCL are scarce. The purpose of this study is to investigate the prognostic value of CT findings to predict clinical outcomes in fifty-one patients with histologically proven PTCL. Patients and methods The clinical data and CT images of all patients were retrospectively reviewed. CT features including number of involvement sites, lesion size, shape, margin, density, peritumoral invasion, intratumoral necrosis, lymph node involvement, and degree of contrast enhancement were evaluated. Univariate and multiple logistic regression analysis were used to determine the association between the clinical outcome and radiologic factors. Results Multiple site involvement, an ill-defined margin with peritumoral invasion, inhomogeneous density, and intratumoral necrosis were found to be associated with poor outcomes in univariate analysis (P < 0.05). An ill-defined margin with peritumoral invasion, was identified as an independent risk sign by further multivariate logistic regression analysis (P < 0.05). The area under the ROC curve of this CT feature was 0.745 (P < 0.05). Conclusions An ill-defined margin with peritumoral invasion was a valuable prognostic factor to predict the worse clinical outcomes in patients with PTCL.

2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography in primary extranodal lymphomas: treatment response evaluation and prognosis

BACKGROUND

We evaluated the role of [18F]FDG PET/CT in tumor response assessment and prognosis of primary extranodal lymphoma (PEL) patients.

METHODS

We examined retrospectively, 56 PEL patients: 31 with aggressive diffuse large B cell lymphoma (DLBCL) and 25 with indolent lymphoma (20 mucosa-associated lymphoid tissue lymphoma and five follicular lymphoma). All patients had undergone [18F]FDG PET/CT at diagnosis (PET-I) and 50 of them also after therapy (PET-II). Moreover, 52 patients were subjected to a mean follow-up period of 76 months.

RESULTS

PET-I was positive in 50 (89%) patients (mean SUVmax 10.3±6.7). In the assessment of tumor response, according to Lugano classification, 45 patients showed complete metabolic response (CMR), four patients had partial metabolic response (PMR) and one had progressive metabolic disease (PMD). Based on 66% ΔSUVmax cut-off, among CMR patients, 41 showed a ΔSUVmax>66% whereas among non-responders, four patients showed a ΔSUVmax<66%. At follow-up, univariate analysis showed that age, performance status, prognostic index, ΔSUVmax and Lugano classification predicted progression-free survival (PFS) (P<0.05), while, performance status, prognostic index, ΔSUVmax and Lugano classification predicted overall survival (OS) (P<0.05). At multivariate analysis only Lugano classification was retained in the model for prediction of both PFS (P<0.05) and OS (P<0.05). By Kaplan-Meier analysis and log-rank testing both PFS and OS were significantly better in patients in CMR as compared to patients in PMR or PMD according to Lugano classification (P<0.01).

CONCLUSIONS

[18F]FDG PET/CT represents a useful tool in the detection of disease response and in the evaluation of outcome in PEL patients.

MRI of diffuse large B-cell non-Hodgkin's lymphoma of the head and neck: comparison of Waldeyer's ring and sinonasal lymphoma

To document the magnetic resonance imaging (MRI) features of diffuse large B-cell lymphoma (DLBCL) in Waldeyer's ring (WR) and the sinonasal (SN) region, and to identify any differences between lymphatic and extra-lymphatic DLBCLs, and predictors of disease beyond the neck. Primary, nodal, and multifocal sites on head and neck MRI were compared between 31 WR and 15 SN DLBCL, and between 27 patients with disease confined to the head and neck and 16 patients with disease beyond the neck, using logistic regression. Compared to SN, WR DLBCLs had significantly smaller primary tumour volumes (p = 0.009), less deep invasion (p = 0.001), and more nodal disease (p = 0.016). Tumour site (WR vs. SN) was an independent predictor of deep invasion (p = 0.007). Nodal and multifocal diseases were predictors of disease beyond the neck (p = 0.027 and 0.011, respectively). Lymphatic WR DLBCLs were less locally aggressive but had greater propensity to nodal spread than extra-lymphatic SN DLBCLs. Nodal and multifocal diseases predicted disease beyond the neck.