Prognostic factors and nomogram for survival prediction in patients with primary pulmonary lymphoma: a SEER population-based study

Treatment outcome, toxicity, and quality of life of patients with bronchus-associated lymphoid tissue lymphoma

The disease failure patterns and optimal treatment of bronchus-associated lymphoid tissue (BALT) lymphoma are unknown. This retrospective study involved 71 patients with primary BALT lymphoma who had received radiotherapy (RT), surgery, immunochemotherapy (IC), or observation. The median follow-up time was 66 months. The 5-year overall survival and lymphoma-specific survival were 91.2% and 96.1%, respectively, and were not significantly different among treatments. The 5-year cumulative incidence of overall failure for RT, surgery, IC, and observation was 0%, 9.7% (p = .160), 30.8% (p = .017), and 31.3% (p = .039). There was no grade ≥3 toxicity in RT group according to the CTCAE 5.0 reporting system. Quality of life (QoL) was at similarly good levels among the treatment groups. BALT lymphoma had a favorable prognosis but persistent risk of relapse after IC or observation. Given the very low disease failure risk and good QoL, RT remains an effective initial treatment for BALT lymphoma.

Single-cell sequencing and transcriptome analyses in the construction of a liquid-liquid phase separation-associated gene model for rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is a disabling autoimmune disease that affects multiple joints. Accumulating evidence suggests that imbalances in liquid-liquid phase separation (LLPS) can lead to altered spatiotemporal coordination of biomolecular condensates, which play important roles in carcinogenesis and inflammatory diseases. However, the role of LLPS in the development and progression of RA remains unclear. Methods: We screened RA and normal samples from GSE12021, GSE55235, and GSE55457 transcriptome datasets and GSE129087 and GSE109449 single-cell sequencing datasets from Gene Expression Omnibus database to investigate the pathogenesis of LLPS-related hub genes at the transcriptome and single cell sequencing levels. Machine learning algorithms and weighted gene co-expression network analysis were applied to screen hub genes, and hub genes were validated using correlation studies. Results: Differential analysis showed that 36 LLPS-related genes were significantly differentially expressed in RA, further random forest and support vector machine identified four and six LLPS-related genes, respectively, and weighted gene co-expression network analysis identified 396 modular genes. Hybridization of the three sets revealed two hub genes, MYC and MAP1LC3B, with AUCs of 0.907 and 0.911, respectively. Further ROC analysis of the hub genes in the GSE55457 dataset showed that the AUCs of MYC and MAP1LC3B were 0.815 and 0.785, respectively. qRT-PCR showed that the expression of MYC and MAP1LC3B in RA synovial tissues was significantly lower than that in the normal control synovial tissues. Correlation analysis between hub genes and the immune microenvironment and single-cell sequencing analysis revealed that both MYC and MAP1LC3B were significantly correlated with the degree of infiltration of various innate and acquired immune cells. Conclusion: Our study reveals a possible mechanism for LLPS in RA pathogenesis and suggests that MYC and MAP1LC3B may be potential novel molecular markers for RA with immunological significance.

A new prognostic nomogram in patients with mucosa-associated lymphoid tissue lymphoma: a multicenter retrospective study

Background

The present study sought to understand how clinical factors and inflammatory biomarkers affected the prognosis of mucosa-associated lymphoid tissue (MALT) lymphoma and develop a predictive nomogram to assist in clinical practice.

Methods

We conducted a retrospective study on 183 cases of newly diagnosed MALT lymphoma from January 2011 to October 2021, randomly divided into two groups: a training cohort (75%); and a validation cohort (25%). The least absolute shrinkage and selection operator (LASSO) regression analysis was combined with multivariate Cox regression analysis to construct a nomogram for predicting the progression-free survival (PFS) in patients with MALT lymphoma. To evaluate the accuracy of the nomogram model, the area under the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used.

Results

The PFS was significantly associated with the Ann Arbor Stage, targeted therapy, radiotherapy, and platelet-to-lymphocyte ratio (PLR) in MALT lymphoma. These four variables were combined to establish a nomogram to predict the PFS rates at three and five years. Importantly, our nomogram yielded good predictive value with area under the ROC curve (AUC) values of 0.841 and 0.763 in the training cohort and 0.860 and 0.879 in the validation cohort for the 3-year and 5-year PFS, respectively. Furthermore, the 3-year and 5-year PFS calibration curves revealed a high degree of consistency between the prediction and the actual probability of relapse. Additionally, DCA demonstrated the net clinical benefit of this nomogram and its ability to identify high-risk patients accurately.

Conclusion

The new nomogram model could accurately predict the prognosis of MALT lymphoma patients and assist clinicians in designing individualized treatments.

Role of 18F-FDG PET/CT in patients affected by pulmonary primary lymphoma

Background

Primary pulmonary lymphoma (PPL) is defined as clonal abnormal hyperplasia of lung parenchyma or bronchial lymphoid tissue originating from bronchial mucosal tissue. However, PPL is rare, which accounts for approximately 3-4% of extraneurotic lymphomas and 0.5-1% of all primary tumors in the lung. Owing to the lack of any typical clinical symptoms and radiological features, it is challenging to accurately diagnose PPL, which affects its clinical management and prognosis. Considering this, herein, we aim to raise awareness of this disease and help physicians understand the role of 18F-FDG PET/CT in the diagnosis of PPL.

Method

A retrospective analysis was performed on the clinical and 18F-FDG PET/CT imaging data of 19 patients diagnosed with PPL by biopsy pathology at our hospital from April 2014 to December 2021.

Results

Of the 19 PPL patients, 15 patients showed clinical symptoms with the most common being fever and cough. In addition, there were 4 cases that had no clinical symptoms, and all of them were MALT lymphoma. In fact, 16 patients were misdiagnosed as lobar pneumonia, lung cancer, tuberculosis, and diffuse interstitial inflammation, representing a misdiagnosis rate of 84.2%. Also, 73.7% were MALT lymphomas, representing the most common pathological pattern, along with 3 DLBCL and 2 T-cell lymphomas. With reguard to CT signs, the air-bronchial sign was found to be the most common, followed by the halo sign and the collapsed leaf sign. On the basis of the predominant radiologic features, lesions were categorized as pneumonic consolidation, nodular/mass type, diffuse interstitial type, and mixed type. The average SUVmax of lesions was 7.23 ± 4.75, the ratio of SUVmax (lesion/liver) was 3.46 ± 2.25, and the ratio of SUVmax (lesion/mediastinal blood pool) was found to be 5.25 ± 3.27. Of interest, the different pathological types of PPL showed different values of 18F-FDG uptake. The 18F-FDG uptake of DLCBL was the most prominent with a SUVmax of 15.33 ± 6.30 and was higher than that of MALT lymphoma with a SUVmax of 5.74 ± 2.65. There appeared similarity in 18F-FDG uptake between MALT lymphoma and T-cell lymphoma. For the SUVmax of lesion, we found statistical significance between MALT lymphoma and DLCBL (P value<0.001). In addition, we also found statistical significance (P value < 0.05) in SUVmax of lesions between pneumonic consolidation type and nodal/mass type, I stage, and other stages.

Conclusions

On 18F-FDG PET/CT images, certain features of PPL morphology and metabolism can be identified that may contribute to a better understanding of this disease. In addition, 18F-FDG PET/CT whole-body imaging has the potential to refine the staging of PPL. Most importantly, functional 18F-FDG PET/CT imaging can readily reflect tumor cell activity, thus allowing for the selection of an optimal biopsy site.

Nomogram individually predicts the risk for distant metastasis and prognosis value in female differentiated thyroid cancer patients: A SEER-based study

Objective

Distant metastasis (DM) is an important prognostic factor in differentiated thyroid cancer (DTC) and determines the course of treatment. This study aimed to establish a predictive nomogram model that could individually estimate the risk of DM and analyze the prognosis of female DTC patients (FDTCs).

Materials and methods

A total of 26,998 FDTCs were retrospectively searched from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 and randomly divided into validation and training cohorts. Univariate and multivariate analyses were performed to screen for prognostic factors and construct a prediction nomogram. The performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), concordance index (C-index), and a calibration curve. The overall survival (OS) and cancer-specific survival (CSS) were evaluated by Kaplan–Meier (K-M) analysis.

Results

A total of 263 (0.97%) FDTCs were reported to have DM. K-M analysis showed the association of multiple-organ metastases and brain involvement with lower survival rates (P &lt; 0.001) in patients. Tumor size, age at diagnosis, thyroidectomy, N1 stage, T3–4 stage, and pathological type were independent predictive factors of DM in FDTCs (all P &lt; 0.001). Similarly, age at diagnosis, Black, DM, T3–4 stage, thyroidectomy, and lung metastasis were determined as independent prognostic factors for FDTCs (all P &lt; 0.001). Several predictive nomograms were established based on the above factors. The C-index, AUC, and calibration curves demonstrated a good performance of these nomogram models.

Conclusion

Our study was successful in establishing and validating nomograms that could predict DM, as well as CSS and OS in individual patients with FDTC based on a large study cohort. These nomograms could enable surgeons to perform individualized survival evaluation and risk stratification for FDTCs.

Nomogram model and risk score predicting overall survival and guiding clinical decision in patients with Hodgkin's lymphoma: an observational study using SEER population-based data

INTRODUCTION

This study developed a prognostic nomogram of Hodgkin lymphoma (HL) for purpose of discussing independent risk factors for HL patients with Surveillance, Epidemiology and End Results (SEER) database.

METHODS

We collected data of HL patients from 2010 to 2015 from the SEER database and divided it into two cohorts: the training and the verification cohort. Then the univariate and the multivariate Cox regression analyses were conducted in the training, the verification as well as the total cohort, after which the intersection of variables with statistical significance was taken as independent risk factors to establish the nomogram. The predictive ability of the nomogram was validated by the Concordance Index. Additionally, the calibration curve and receiver operating characteristic curve were implemented to evaluate the accuracy and discrimination. Finally, we obtained 1-year, 3-year and 5-year survival rates of HL patients.

RESULTS

10 912 patients were eligible for the study. We discovered that Derived American Joint Committee on Cancer (AJCC) Stage Group, lymphoma subtype, radiotherapy and chemotherapy were four independent risk factors affecting the prognosis of HL patients. The 1-year, 3-year and 5-year survival rates for high-risk patients were 85.4%, 79.9% and 76.0%, respectively. It was confirmed that patients with stage I or II had a better prognosis. Radiotherapy and chemotherapy had a positive impact on HL outcomes. However, patients with lymphocyte-depleted HL were of poor prognosis.

CONCLUSIONS

The nomogram we constructed could better predict the prognosis of patients with HL. Patients with HL had good long-term outcomes but novel therapies are still in need for fewer complications.

Nomograms for Predicting Cancer-Specific Survival of Patients with Gingiva Squamous Cell Carcinoma: A Population-Based Study

OBJECTIVE

The use of the traditional American Joint Committee on Cancer (AJCC) staging system alone has limitations in predicting the survival of gingiva squamous cell carcinoma (GSCC) patients. We aimed to establish a comprehensive prognostic nomogram with a prognostic value similar to the AJCC system.

METHODS

Patients were identified from SEER database. Variables were selected by a backward stepwise selection method in a Cox regression model. A nomogram was used to predict cancer-specific survival rates for 3, 5 and 10 years in patients with GSCC. Several basic features of model validation were used to evaluate the performance of the survival model: consistency index (C-index), receiver operating characteristic (ROC) curve, calibration chart, net weight classification improvement (NRI), comprehensive discriminant improvement (IDI) and decision curve analysis (DCA).

RESULTS

Multivariate analyses revealed that age, race, marital status, insurance, AJCC stage, pathology grade and surgery were risk factors for survival. In particular, the C-index, the area under the ROC curve (AUC) and the calibration plots showed good performance of the nomogram. Compared to the AJCC system, NRI and IDI showed that the nomogram has improved performance. Finally, the nomogram's 3-year and 5-year and 10-year DCA curves yield net benefits higher than traditional AJCC, whether training set or a validation set.

CONCLUSION

We developed and validated the first GSCC prognosis nomogram, which has a better prognostic value than the separate AJCC staging system. Overall, the nomogram of this study is a valuable tool for clinical practice to consult patients and understand their risk for the next 3, 5 and 10 years.

Analysis of Clinical Features, Pathological Features and Misdiagnosis of Pulmonary Lymphoma

Primary pulmonary lymphoma is a relatively rare extranodal lymphoma, and the incidence rate has increased in recent years. In the past, the disease mainly relied on surgery to obtain the pathological basis, so the clinical misdiagnosis rate was high. How to improve its early diagnosis and treatment has attracted much attention. By exploring the imaging manifestations of primary pulmonary lymphoma, we can further understand and improve the imaging diagnosis level of primary pulmonary lymphoma. This paper discusses the classification, imaging manifestations, diagnosis and identification of pulmonary lymphoma. The clinical data and imaging findings of primary pulmonary lymphoma diagnosed in a hospital were retrospectively summarized, and their imaging features were analyzed. We observe the clinicopathological characteristics and immunohistochemical phenotypes of multiple masses with cavitation type primary lung lymphoma, and analyze the virus and imaging characteristics of hybridization. The results of the study show that the CT (Computed Tomography) manifestations of primary lung lymphoma are diverse. The characteristics of cross-leaf distribution are more characteristic, and enhanced scanning lesions are usually mild to moderate. In the case of simultaneous masses and pneumonialike consolidation in the lungs, this disease needs to be considered.

Clinicopathological characteristics and prognostic factors of primary pulmonary lymphoma

Background

Primary pulmonary lymphoma (PPL) is a rare extranodal lymphoma originating from the lung, accounting for 0.5–1.0% of primary lung malignant tumors. Previous case reports or cohort studies included a limited sample size; therefore, the understanding of the disease remains inadequate, and clinical data regarding PPL are limited.

Methods

Patients with PPL diagnosed histologically and radiologically between January 2000 and December 2019 at our center were retrospectively analyzed.

Results

In total, 90 consecutive cases were included in this research. Forty-seven (52.2%) patients were female, and the median age was 54 years old. Non-Hodgkin’s lymphoma (PPNHL) was the most common type of PPL (71/90, 78.9%), and mucosa-associated lymphoid tissue (MALT) lymphoma was the most common pathological subtype of PPNHL (56.3%) followed by diffuse large B-cell lymphoma (DLBCL) (32.4%). Thirty-nine (43.3%) patients underwent surgical treatment, and the others received chemotherapy alone or combined with radiotherapy. The estimated 5-year overall survival (OS) rates of MALT lymphoma and non-MALT lymphoma were 68.9% and 65.9%, respectively. Univariate analysis of PPL showed that clinicopathological features that significantly correlated with worse OS were age over 60 years (P=0.006<0.05), elevated LDH (P=0.029<0.05) and β2-MG (P=0.048<0.05) levels, clinical stage II2E and greater (P=0.015<0.05), and nonsurgical treatment (P=0.046<0.05). Age (P=0.013<0.05) was an independent prognostic factor for the 5-year OS of patients through multivariate analysis.

Conclusions

Age over 60 years old, elevated LDH and β2-MG levels, clinical stage II2E disease or higher, and nonsurgical treatment were associated with poor prognosis in patients with PPL. Age can be used as a potential independent prognostic factor for PPL.

Pulmonary mucosa-associated lymphoid tissue lymphoma: CT findings and pathological basis

BACKGROUND

Pulmonary mucosa-associated lymphoid tissue lymphoma (MALToma) is the most frequent subset of primary pulmonary lymphoma. This study aimed to identify radiologic characteristics of pulmonary MALToma based on computed tomography (CT) observations and pathologic features, and further investigate its prognosis.

METHODS

Sixty-six patients (55.4 ± 10.9 years; 51.5% male) diagnosed as pulmonary MALToma by pathology were retrospectively enrolled. According to distributions and features of lesions shown on CT, patients were divided into three patterns, including single nodular/mass, multiple nodular/mass, and pneumonia-like consolidative.

RESULTS

Variety of the location and extent of the lymphomatous infiltration accounted for different characteristics demonstrated at CT. The pneumonia-like consolidative pattern was the most frequent pattern observed in 42 patients (63.6%), followed by single nodular/mass (21.2%) and multiple nodular/mass (15.2%). CT features included air bronchogram (72.7%), well-marginated halo sign (53.0%), coarse spiculate with different lengths (72.7%), angiogram sign (77.1% of 35 patients), peribronchovascular thickening (48.5%), irregular cavitation (16.7%) and pulmonary cyst (7.6%). The estimated 5-year cumulative overall survival rate of pulmonary MALToma was 100.0%.

CONCLUSIONS

Pulmonary MALToma demonstrates several characteristics at CT. Identification of the significant pulmonary abnormalities of this indolent disease entity might be helpful for early diagnosis and optimal treatment.

Lung cancer survival period prediction and understanding: Deep learning approaches

INTRODUCTION

Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this paper, by focusing on lung cancer patients, we build several survival prediction models using deep learning techniques to tackle both cancer survival classification and regression problems. We also conduct feature importance analysis to understand how lung cancer patients' relevant factors impact their survival periods. We contribute to identifying an approach to estimate survivability that are commonly and practically appropriate for medical use.

METHODOLOGIES

We have compared the performance across three of the most popular deep learning architectures - Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) while comparing the performing of deep learning models against traditional machine learning models. The data was obtained from the lung cancer section of Surveillance, Epidemiology, and End Results (SEER) cancer registry.

RESULTS

The deep learning models outperformed traditional machine learning models across both classification and regression approaches. We obtained a best of 71.18 % accuracy for the classification approach when patients' survival periods are segmented into classes of '<=6 months',' 0.5 - 2 years' and '>2 years' and Root Mean Squared Error (RMSE) of 13.5 % andR² value of 0.5 for the regression approach for the deep learning models while the traditional machine learning models saturated at 61.12 % classification accuracy and 14.87 % RMSE in regression.

CONCLUSIONS

This approach can be a baseline for early prediction with predictions that can be further improved with more temporal treatment information collected from treated patients. In addition, we evaluated the feature importance to investigate the model interpretability, gaining further insight into the survival analysis models and the factors that are important in cancer survival period prediction.

The incidence and survival outcomes of patients with primary cardiac lymphoma: A SEER-based analysis

This study aimed to evaluate the incidence and prognosis of primary cardiac lymphoma (PCL) by using the Surveillance, Epidemiology, and End Results Program (SEER) database. Patients diagnosed with PCL and the disease incidence in the SEER database from 1975 to 2016 were included. Overall survival (OS) and cause-specific survival (CSS) curves were calculated using the Kaplan-Meier method and compared by the log-rank test. Univariate and multivariable Cox proportional hazard regression analyses were used to identify associations with outcome measures. The incidence of PCL was 0.011/100 000, and a predominance of elderly and male patients was observed. A total of 144 patients were enrolled. The median age of onset was 68 (9-96) years, including 80 (55.6%) males and 64 (44.4%) females. Multivariate analysis revealed that age and chemotherapy were independent prognostic factors for OS (both P < .05). Ann Arbor stage and chemotherapy were independent prognostic factors for CSS (both P < .05). In terms of treatment modality, chemotherapy combined with surgery was an independent protective factor for OS and CSS (both P < .05). For patients with primary cardiac diffuse large B-cell lymphoma (cardiac DLBCL), multivariate analysis also showed that age, Ann Arbor stage, and chemotherapy were all independent prognostic factors for OS and CSS (all P < .05). Chemotherapy combined with surgery was associated with a significant benefit in terms of OS and CSS (both P < .05). Our study confirmed that older age and advanced Ann Arbor stage were independent risk factors for PCL, and treatment with chemotherapy or cooperation with surgery resulted in better long-term survival.