Clinical characteristics and disease-specific prognostic nomogram for primary gliosarcoma: a SEER population-based analysis

Metastatic secondary gliosarcoma: patient series

BACKGROUND

Gliosarcoma is a rare and highly malignant cancer of the central nervous system with the ability to metastasize. Secondary gliosarcoma, or the evolution of a spindle cell-predominant tumor after the diagnosis of a World Health Organization grade IV glioblastoma, has also been shown to metastasize. There is little information on metastatic secondary gliosarcoma.

OBSERVATIONS

The authors present a series of 7 patients with previously diagnosed glioblastoma presenting with recurrent tumor and associated metastases with repeat tissue diagnosis consistent with gliosarcoma. The authors describe the clinical, imaging, and pathological characteristics in addition to carrying out a systematic review on metastases in secondary gliosarcoma.

LESSONS

The present institutional series and the systematic review of the literature show that metastatic secondary gliosarcoma is a highly aggressive disease with a poor prognosis.

Predicting skip metastasis in lateral lymph nodes of papillary thyroid carcinoma based on clinical and ultrasound features

Background

Skip metastasis in papillary thyroid cancer (PTC), defined as lateral lymph node metastasis (LLNM) without the involvement of central lymph node metastasis (CLNM), is generally unpredictable. Our study aimed to develop a model to predict skip metastasis by using clinicopathological and ultrasound factors of PTC.

Methods

We retrospectively reviewed the medical records of patients who underwent total thyroidectomy and central lymph node dissection (CLND) plus lateral lymph node dissection (LLND) between January 2019 and December 2021 at the First Affiliated Hospital of Soochow University. Furthermore, univariate and multivariate analyses assessed the clinical and ultrasound risk factors. Receiver operating characteristic (ROC) curves were used to find the optimal cut-off values for age and dominant nodule diameter. Multivariate logistic regression analysis results were used to construct a nomogram and were validated internally.

Results

In all patients, the skip metastasis rate was 15.4% (41/267). Skip metastasis was more frequently found in patients with a tumour size ≤10 mm (OR 0.439; P = 0.033), upper tumour location (OR 3.050; P=0.006) and fewer CLNDs (OR 0.870; P = 0.005). After analysing the clinical and ultrasound characteristics of the tumour, five factors were ultimately associated with lateral lymph node skip metastasis and were used to construct the model. These factors were an age >40 years, tumour diameter <9.1 mm, upper tumour location, non-smooth margin and extrathyroidal extension. The internally evaluated calibration curves indicated an excellent correlation between the projected and actual skip metastasis probability. The nomogram performed well in discrimination, with a concordance index of 0.797 (95% CI, 0.726 to 0.867).

Conclusions

This study screened for predictors of skip metastasis in PTC and established a nomogram that effectively predicted the risk of potential skip metastasis in patients preoperatively. The method can predict and distinguish skip metastases in PTC in a simple and inexpensive manner, and it may have future therapeutic utility.

Prognostic Factors of Gliosarcoma in the Real World: A Retrospective Cohort Study

Purpose

Gliosarcoma is a histopathological variant of glioblastoma, which is characterized by a biphasic growth pattern consisting of glial and sarcoma components. Owing to its scarcity, data regarding the impact of available treatments on the clinical outcomes of gliosarcoma are inadequate. The purpose of this retrospective cohort study was to analyze the prognostic factors of gliosarcoma.

Methods

By screening the clinical database of neurosurgical cases at a single center, patients with gliosarcoma diagnosed histologically from 2013 to 2021 were identified. Clinical, pathological, and molecular data were gathered founded on medical records and follow-up interviews. Prognostic factors were derived using the Cox proportional hazards model with backward stepwise regression analysis.

Results

Forty-five GSM patients were included. Median overall survival was 25.6 months (95% CI 8.0-43.1), and median relapse-free survival was 15.2 months (95% CI 9.7-20.8). In multivariable analysis, total resection (p = 0.023, HR = 0.192, 95% CI 0.046-0.797) indicated an improved prognosis. And low expression of Ki-67 (p = 0.059, HR = 2.803, 95% CI 0.963-8.162) would be likely to show statistical significance. However, there might be no statistically significant survival benefit from radiotherapy with concurrent temozolomide (n = 33, 73.3%, log-rank p = 0.99) or adjuvant temozolomide (n = 32, 71.1%, log-rank p = 0.74).

Conclusion

This single-center retrospective study with a limited cohort size has demonstrated the treatment of gross total resection and low expression of Ki-67 which are beneficial for patients with GSM, while radiotherapy or temozolomide is not.

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

BACKGROUND

Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.

METHODS

We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models.

RESULTS

Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available.

CONCLUSIONS

The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

Gliosarcoma with direct involvement of the oculomotor nerve: Case report and literature review

Gliosarcoma is a rare malignant brain tumor, characterized by a biphasic tissue pattern with alternating areas displaying glial and mesenchymal differentiation. We first report a case of temporo-mesial gliosarcoma, extended to the crural and ambient cisterns, with direct involvement of the ipsilateral third cranial nerve and encasement of anterior choroidal, posterior communicant and posterior cerebral arteries, presenting without symptoms of peripheral neuropathy. A 61-year-old woman with 1-month history of intense bilateral frontal-temporal headache resistant to pharmacological therapy and paresis of the left lower midface underwent surgical resection, through pterional trans-sylvian approach, of a right temporo-mesial gliosarcoma which directly involved the ipsilateral oculomotor nerve. Reported cases of gliomas with direct involvement of a cranial nerve, from the third to the twelfth, are very rare, whit no cases of gliosarcoma described. Because of its rarity, sometimes this entity is not considered as diagnostic hypothesis and is misdiagnosed, both during preoperative diagnostic evaluation and during the surgery. Gliosarcoma is a strong challenge for neurosurgeons and neurooncologists because of low incidence, poor prognosis and limited reported cases on literature. This case shows unique features for localization, pattern of growth and clinical presentation.

Computed Tomography, Magnetic Resonance Imaging, and Pathological Features of Gliosarcoma

OBJECTIVE

To investigate the clinical, imaging, and pathological features of gliosarcoma.

METHODS

The clinical data of 14 patients with gliosarcoma confirmed by surgery and pathology at our hospital between 2010 and 2021 were analyzed retrospectively, and the relevant literature was reviewed.

RESULTS

In all 14 cases, the gliosarcoma was located in the supratentorial brain parenchyma and involved a single lesion. There were more male patients (64.3%) than female patients (35.7%), and 57.1% of all the patients were 40-60 years of age. The prognosis of all 14 patients was poor, and the average survival time was approximately seven months. The computed tomography findings revealed mostly mixed density lesions, and some cases were complicated with bleeding. The magnetic resonance imaging revealed irregularly shaped mass lesions of different sizes, with uneven or circular enhancement. Cystic degeneration and necrosis could be seen in all the masses, some of which showed signs of bleeding and were surrounded by different degrees of edema and space-occupying effects. The pathological examination revealed that the tumors had bidirectional differentiation of the glial and sarcoma components, while the immunohistochemistry examination revealed glial fibrillary acidic protein-positive and reticular fiber-positive staining in the sarcoma.

CONCLUSION

The clinical manifestations of gliosarcoma are nonspecific, but imaging reveals that the condition has certain characteristics, typically consisting of a huge supratentorial mass, with an irregular heterogeneous periphery or obvious mass-like augmentation after enhancement. The final diagnosis depends on the results of a pathological examination.

Chasing a rarity: a retrospective single-center evaluation of prognostic factors in primary gliosarcoma

Background and purpose

Primary gliosarcoma (GS) is a rare variant of IDH-wildtype glioblastoma multiforme. We performed a single-center analysis to identify prognostic factors.

Patients and methods

We analyzed the records of 26 patients newly diagnosed with primary WHO grade IV GS. Factors of interest were clinical and treatment data, as well as molecular markers, time to recurrence, and time to death.

Results

Median follow-up was 9 months (range 5–21 months). Gross total resection did not lead to improved survival, most likely due to the relatively small sample size. Low symptom burden at the time of diagnosis was associated with longer PFS (P = 0.023) and OS (P = 0.018). Median OS in the entire cohort was 12 months. Neither MGMT promoter hypermethylation nor adjuvant temozolomide therapy influenced survival, consistent with some previous reports.

Conclusion

In this retrospective study, patients exhibiting low symptom burden at diagnosis showed improved survival. None of the other factors analyzed were associated with an altered outcome.

Identification and characterization of N6-methyladenosine modification of circRNAs in glioblastoma

This research systematically profiled the global N6‐methyladenosine modification pattern of circular RNAs (circRNAs) in glioblastoma (GBM). Based on RNA methylation sequencing (MeRIP sequencing or N6‐methyladenosine sequencing) and RNA sequencing, we described the N6‐methyladenosine modification status and gene expression of circRNAs in GBM and normal brain tissues. N6‐methyladenosine–related circRNAs were immunoprecipitated and validated by real‐time quantitative PCR. Bioinformatics analysis and related screening were carried out. Compared with those of the NC group, the circRNAs from GBM exhibited 1370 new N6‐methyladenosine peaks and 1322 missing N6‐methyladenosine peaks. Among the loci associated with altered N6‐methyladenosine peaks, 1298 were up‐regulated and 1905 were down‐regulated. The N6‐methyladenosine level tended to be positively correlated with circRNA expression. Bioinformatics analysis was used to predict the biological function of N6‐methyladenosine–modified circRNAs and the corresponding signalling pathways. In addition, through PCR validation combined with clinical data mining, we identified five molecules of interest (BUB1, C1S, DTHD1, F13A1 and NDC80) that could be initial candidates for further study of the function and mechanism of N6‐methyladenosine–mediated GBM development. In conclusion, our findings demonstrated the N6‐methyladenosine modification pattern of circRNAs in human GBM, revealing the possible roles of N6‐methyladenosine–mediated novel noncoding RNAs in the origin and progression of GBM.

Optic pathway gliosarcoma: A very rare location for a rare disease

Gliosarcoma, a variant of glioblastoma, is a rare and aggressive tumor of the central nervous system (CNS) composed of glial and sarcomatous tissues. Up to now, there are only 2 reported cases of gliosarcoma of the optical pathway. We report a case from March 2018 of a 53-year-old male patient presented with 6 months’ of right fronto-orbital pulsatile headache, behavior changes, and visual loss. The MRI study showed an expansile optic pathway lesion involving the chiasm and right optic nerve. The diagnosis of gliosarcoma was obtained by open brain biopsy and immunohistochemical analysis. Although gliosarcoma is rare, it should be considered a differential diagnosis even in optic pathway tumors in older patients. The experience of the neuropathologist with a trained eye can be the differential in the accurate diagnostic process.

Optic pathway, Gliosarcoma, Glioblastoma, Magnetic resonance imaging

Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study

Introduction

We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas.

Methods

The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes (C-indexes) and calibration plots.

Results

All 813 patients were randomly divided into a training group (n = 572) and a validation group (n = 241). After univariate and multivariate Cox regression, a nomogram was constructed based on a new model containing the predictive variables of age, site, grade, tumor size, histology, stage, and use of surgery, radiotherapy, or chemotherapy. The prediction model provided excellent C-indexes (0.86 and 0.77 in the training and validation groups, respectively). The good discrimination and calibration of the nomograms were demonstrated for both the training and validation groups.

Conclusions

The nomograms precisely and individually predict the overall survival of patients with limb chondrosarcomas and could assist personalized prognostic evaluation and individualized clinical decision-making.

Do gliosarcomas have distinct imaging features on routine MRI?

PURPOSE

The aim of this study was the development and external validation of a logistic regression model to differentiate gliosarcoma (GSC) and glioblastoma multiforme (GBM) on standard MR imaging.

METHODS

A univariate and multivariate analysis was carried out of a logistic regression model to discriminate patients histologically diagnosed with primary GSC and an age and sex-matched group of patients with primary GBM on presurgical MRI with external validation.

RESULTS

In total, 56 patients with GSC and 56 patients with GBM were included. Evidence of haemorrhage suggested the diagnosis of GSC, whereas cystic components and pial as well as ependymal invasion were more commonly observed in GBM patients. The logistic regression model yielded a mean area under the curve (AUC) of 0.919 on the training dataset and of 0.746 on the validation dataset. The accuracy in the validation dataset was 0.67 with a sensitivity of 0.85 and a specificity of 0.5.

CONCLUSIONS

Although some imaging criteria suggest the diagnosis of GSC or GBM, differentiation between these two tumour entities on standard MRI alone is not feasible.

Clinical characteristics and overall survival nomogram of second primary malignancies after prostate cancer, a SEER population-based study

Prostate cancer (PCa) is the most prevalent cancer among males and the survival period of PCa has been significantly extended. However, the probability of suffering from second primary malignancies (SPMs) has also increased. Therefore, we downloaded SPM samples from the SEER database and then retrospectively analyzed the general characteristics of 34,891 PCa patients diagnosed between 2000 and 2016. After excluding cases with unknown clinical information, 2203 patients were used to construct and validate the overall survival (OS) nomogram of SPM patients after PCa. We found that approximately 3.69% of PCa patients were subsequently diagnosed with SPMs. In addition, the three most prevalent sites of SPM were respiratory and intrathoracic organs, skin, and hematopoietic system. The top three histological types of SPMs were squamous cell carcinoma, adenoma and adenocarcinoma, nevi and melanoma. Through univariate and multivariate Cox regression analysis, we found that the site of SPM, age, TNM stage, SPM surgery history, and PCa stage were associated with the OS of SPM. By virtue of these factors, we constructed a nomogram to predict the OS of SPM. The C-index in the training set and validation set were 0.824 (95CI, 0.806-0.842) and 0.862 (95CI, 0.840-0.884), respectively. Furthermore, we plotted the receiver operating characteristic curve (ROC) and the area under curve (AUC) which showed that our model performed well in assessing the 3-year (0.861 and 0.887) and 5-year (0.837 and 0.842) OS of SPMs in the training and validation set. In summary, we investigated the general characteristics of SPMs and constructed a nomogram to predict the prognosis of SPM following PCa.

13-lncRNAs Signature to Improve Diagnostic and Prognostic Prediction of Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common type of cancer with a high mortality rate and is usually detected at the middle or late stage, missing the optimal treatment period. The current study aims to identify potential long non-coding RNA (lncRNAs) biomarkers that contribute to the diagnosis and prognosis of HCC.

METHODS

The differentially expressed lncRNAs (DElncRNAs) in HCC patients were detected from the Cancer Genome Atlas (TCGA) dataset. LncRNAs signature was screened by LASSO regression, univariate, and multivariate Cox regression. The models for predicting diagnosis and prognosis were established, respectively. The prognostic model was evaluated by Kaplan-Meier survival curve receiver operating characteristic (ROC) curve and stratified analysis. The diagnostic model was validated by ROC. The lncRNAs signature was further demonstrated by functional enrichment analysis.

RESULTS

We found the 13-lncRNAs signature that had a good performance in predicting prognosis and could help to improve the value of diagnosis. In the training set, testing set, and entire cohort, the low-risk group had longer survival than the high-risk group (median OS: 3124 vs. 649 days, 2456 vs. 770 days and 3124 vs. 755 days). It performed well in 1-, 3-, and 5-year survival prediction. 13-lncRNAs-based risk score, age, and race were good predictors of prognosis. The AUC of diagnosis was 0.9487, 0.9265, and 0.9376, respectively. Meanwhile, the 13-lncRNAs were involved in important pathways, including the cell cycle and multiple metabolic pathways.

CONCLUSION

In our study, the 13-lncRNAs signature may be a potential marker for the prognosis of HCC and improve the diagnosis.

A nomogram to predict skip metastasis in papillary thyroid cancer

Background

Skip metastases are defined as lateral lymph node metastasis (LNM) without the involvement of central LNM in papillary thyroid cancer (PTC), and it is difficult to predict in clinical practice. Our study aimed to investigate the risk factors of skip metastasis and establish a nomogram for predicting the probability of skip metastasis in PTC patients.

Patients and methods

A total of 378 consecutive PTC patients with clinically suspected LNM who underwent modified radical neck dissection (MRND) from March 2018 to July 2019 in our hospital were enrolled. Univariate and multivariate analyses were used to examine risk factors of skip metastasis, and a nomogram prediction model was established and internally validated.

Results

The incidence of skip metastases was 11.6% (44/378). Primary tumor size of ≤ 1 cm (OR = 2.703; 95% CI, 1.342–5.464; P = 0.005), age (OR = 1.051; 95% CI, 1.017–1.805; P = 0.005), and primary tumor location in the upper portion (OR = 6.799; 95% CI, 2.710–17.060; P < 0.001) were found to be independent risk factors for skip metastasis in PTC patients. A nomogram based upon these predictors performed well. The area under the curve (AUC) was 0.806 (95% CI, 0.736–0.876), and the P value of the Hosmer-Lemeshow goodness of fit test was 0.66. Decision curve analysis revealed that the nomogram was clinically useful.

Conclusion

Based on the risk factors of skip metastasis, a high-performance nomogram was established, which can provide an individual risk assessment and can guide treatment decisions for patients.

Survival and analysis of prognostic factors for hepatoblastoma: based on SEER database

Background

The goal of this study is to assess the newest survival of hepatoblastoma (HB) and the risk factors which impacted on survival by using the Surveillance, Epidemiology and End Results (SEER) database, also calculate the incidence of HB in recent years.

Methods

We calculate age-adjusted incidence of HB by using SEER 21 registries. Age, sex, race, tumor size, macrovascular involvement, multifocal tumor, distant metastasis, the way of treatment, and the survival were collected for survival and analysis of prognostic factors in SEER 18 registries. Survival curves, according to different factors, were obtained by Kaplan-Meier estimates. Multivariable Cox regression models were also built.

Results

The overall age-adjusted incidence of HB was 0.19 patients per 100,000 children with a statistically significant increase per year. Overall survival (OS) at 1-, 3- and 5-year for all patients were 89.3%, 84.6%, and 81.9%, respectively. Multivariate analysis showed tumor size >5 cm [hazard ratio (HR), 8.271; 95% confidence interval (CI), 1.134-60.310], multiple tumors (HR, 2.578; 95% CI, 1.424-4.668) and no-surgery treatment (HR, 7.520; 95% CI, 4.121-13.724) were independent indicators of poor prognosis. Only the age ≥2-year-old (HR, 3.240; 95% CI, 1.433-7.326) and multiple tumors (HR, 2.395; 95% CI, 1.057-5.430) were the risk factors for the surgical treatment group.

Conclusions

The survival of patients with HB has been greatly improved in the recent years, and at the same time, due to the application of better chemotherapy, we should re-evaluate the traditional risk indicators of prognosis in order to better apply to the clinical.