Mesenchymal chondrosarcoma: prognostic factors and outcome in 113 patients. A European Musculoskeletal Oncology Society study

BACKGROUND: Mesenchymal chondrosarcoma (MCS) is a distinct, very rare sarcoma with little evidence supporting treatment recommendations. PATIENTS AND METHODS: Specialist centres collaborated to report prognostic factors and outcome for 113 patients. RESULTS: Median age was 30 years (range: 11-80), male/female ratio 1.1. Primary sites were extremities (40%), trunk (47%) and head and neck (13%), 41 arising primarily in soft tissue. Seventeen patients had metastases at diagnosis. Mean follow-up was 14.9 years (range: 1-34), median overall survival (OS) 17 years (95% confidence interval (CI): 10.3-28.6). Ninety-five of 96 patients with localised disease underwent surgery, 54 additionally received combination chemotherapy. Sixty-five of 95 patients are alive and 45 progression-free (5 local recurrence, 34 distant metastases, 11 combined). Median progression-free survival (PFS) and OS were 7 (95% CI: 3.03-10.96) and 20 (95% CI: 12.63-27.36) years respectively. Chemotherapy administration in patients with localised disease was associated with reduced risk of recurrence (P=0.046; hazard ratio (HR)=0.482 95% CI: 0.213-0.996) and death (P=0.004; HR=0.445 95% CI: 0.256-0.774). Clear resection margins predicted less frequent local recurrence (2% versus 27%; P=0.002). Primary site and origin did not influence survival. The absence of metastases at diagnosis was associated with a significantly better outcome (P<0.0001). Data on radiotherapy indications, dose and fractionation were insufficiently complete, to allow comment of its impact on outcomes. Median OS for patients with metastases at presentation was 3 years (95% CI: 0-4.25). CONCLUSIONS: Prognosis in MCS varies considerably. Metastatic disease at diagnosis has the strongest impact on survival. Complete resection and adjuvant chemotherapy should be considered as standard of care for localised disease

An Easy-to-Use Prognostic Model for Survival Estimation for Patients with Symptomatic Long Bone Metastases

BACKGROUND: A survival estimation for patients with symptomatic long bone metastases (LBM) is crucial to prevent overtreatment and undertreatment. This study analyzed prognostic factors for overall survival and developed a simple, easy-to-use prognostic model. METHODS: A multicenter retrospective study of 1,520 patients treated for symptomatic LBM between 2000 and 2013 at the radiation therapy and/or orthopaedic departments was performed. Primary tumors were categorized into 3 clinical profiles (favorable, moderate, or unfavorable) according to an existing classification system. Associations between prognostic variables and overall survival were investigated using the Kaplan-Meier method and multivariate Cox regression models. The discriminatory ability of the developed model was assessed with the Harrell C-statistic. The observed and expected survival for each survival category were compared on the basis of an external cohort. RESULTS: Median overall survival was 7.4 months (95% confidence interval [CI], 6.7 to 8.1 months). On the basis of the independent prognostic factors, namely the clinical profile, Karnofsky Performance Score, and presence of visceral and/or brain metastases, 12 prognostic categories were created. The Harrell C-statistic was 0.70. A flowchart was developed to easily stratify patients. Using cutoff points for clinical decision-making, the 12 categories were narrowed down to 4 categories with clinical consequences. Median survival was 21.9 months (95% CI, 18.7 to 25.1 months), 10.5 months (95% CI, 7.9 to 13.1 months), 4.6 months (95% CI, 3.9 to 5.3 months), and 2.2 months (95% CI, 1.8 to 2.6 months) for the 4 categories. CONCLUSIONS: This study presents a model to easily stratify patients with symptomatic LBM according to their expected survival. The simplicity and clarity of the model facilitate and encourage its use in the routine care of patients with LBM, to provide the most appropriate treatment for each individual patient. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence

Aggressive fibromatosis of the head and neck: a new classification based on a literature review over 40 years (1968-2008)

BACKGROUND: Fibromatosis is an aggressive fibrous tumor of unknown etiology that is, in some cases, lethal. Until now, there has been no particular classification for the head and neck. Therefore, the aim of the present study was to review the current literature in order to propose a new classification for future studies. METHODS: An evidence-based literature review was conducted from the last 40 years regarding aggressive fibromatosis in the head and neck. Studies that summarized patients' data without including individual data were excluded. RESULTS: Between 1968 and 2008, 179 cases with aggressive fibromatosis of the head and neck were published. The male to female ratio was 91 to 82 with a mean age of 16.87 years, and 57.32% of the described cases that involved the head and neck were found in patients under 11 years. The most common localization was the mandible, followed by the neck. All together, 143 patients were followed up, and in 43 (30.07%), a recurrence was seen. CONCLUSION: No clear prognostic factors for recurrence (age, sex, or localization) were observed. A new classification with regard to hormone receptors and bone involvement could improve the understanding of risk factors and thereby assist in future studies

Modified OPTIModel with oligometastatic disease for the prediction of overall survival of patients with renal cell cancer and symptomatic long bone metastases

Aims: For patients with long bone metastasis (LBM), we have previously developed OPTIModel. In this study, we investigated whether the OPTIModel could be improved for patients with metastatic renal cell cancer (mRCC) by including oligometastatic bone metastases (OBM) as a risk factor. Methods:Patients with mRCC and symptomatic LBMs were included in a retrospective and prospective multicenter cohort. Bone metastases (BMs) were categorized as: solitary (SBM), limited BMs (2–4 BMs) or diffuse BMs (DBM; &gt;4 BMs). OBM were defined as ≤ 4 BMs. Overall survival was estimated using Kaplan Meier method. Effect of risk factors on overall survival were assessed using multivariate Cox regression model. Based on these results, the OPTIModel was modified. To assess the discriminatory ability, Harrell's C-statistic was used. Results: 178 patients were included. Overall, median overall survival was 12.1 months (95 % confidence interval (CI): 8.8–15.3). Median survival for SBM (n = 53, 29.8 %), limited BMs (n = 60, 33.7 %) and DBMs (n = 65, 36.5 %) was 19.6 months (95 %CI: 6.8–32.4), 14.8 months (95 %CI: 7.6–21.9) and 6.1 months (95 %CI: 2.7–9.5), respectively. Median survival was 16.3 months (95 %CI: 10.6–22.0) in patients with OBM (n = 113, 63.5 %), with a hazard ratio of 2.11 (95 %CI: 1.44–3.09) compared to patients with DBM. Including OBM in the OPTIModel for mRCC improved C-statistic from 0.585 (standard error (SE) = 0.027) to 0.618 (SE = 0.024). Conclusion: Both SBM and limited BMs were associated with a longer overall survival in patients with mRCC and symptomatic LBMs. The modified OPTIModel for mRCC with inclusion of oligometastatic disease could guide decisions about local treatment of symptomatic LBMs.</p

Development and external validation of a dynamic prognostic nomogram for primary extremity soft tissue sarcoma survivors.

Background:Prognostic nomograms for patients with extremity soft tissue sarcoma (eSTS) typically predict survival or the occurrence of local recurrence or distant metastasis at time of surgery. Our aim was to develop and externally validate a dynamic prognostic nomogram for overall survival in eSTS survivors for use during follow-up. Methods:All primary eSTS patients operated with curative intent between 1994 and 2013 at three European and one Canadian sarcoma centers formed the development cohort. Patients with Fédération Française des Centres de Lutte Contre le Cancer (FNCLCC) grade II and grade III eSTS operated between 2000 and 2016 at seven other European reference centers formed the external validation cohort. We used a landmark analysis approach and a multivariable Cox model to create a dynamic nomogram; the prediction window was fixed at five years. A backward procedure based on the Akaike Information Criterion was adopted for variable selection. We tested the nomogram performance in terms of calibration and discrimination. Findings:The development and validation cohorts included 3740 and 893 patients, respectively. The variables selected applying the backward procedure were patient's age, tumor size and its interaction with landmark time, tumor FNCLCC grade and its interaction with landmark time, histology, and both local recurrence and distant metastasis (as first event) indicator variables. The nomogram showed good calibration and discrimination. Harrell C indexes at different landmark times were between 0.776 (0.761-0.790) and 0.845 (0.823-0.862) in the development series and between 0.675 (0.643-0.704) and 0.810 (0.775-0.844) in the validation series. Interpretation:A new dynamic nomogram is available to predict 5-year overall survival at different times during the first three years of follow-up in patients operated for primary eSTS. This nomogram allows physicians to update the individual survival prediction during follow-up on the basis of baseline variables, time elapsed from surgery and first-event history

Advancements in Neuroendocrine Neoplasms: Imaging and Future Frontiers

Neuroendocrine neoplasms (NENs) are a diverse group of tumors with varying clinical behaviors. Their incidence has risen due to increased awareness, improved diagnostics, and aging populations. The 2019 World Health Organization classification emphasizes integrating radiology and histopathology to characterize NENs and create personalized treatment plans. Imaging methods like CT, MRI, and PET/CT are crucial for detection, staging, treatment planning, and monitoring, but each of them poses different interpretative challenges and none are immune to pitfalls. Treatment options include surgery, targeted therapies, and chemotherapy, based on the tumor type, stage, and patient-specific factors. This review aims to provide insights into the latest developments and challenges in NEN imaging, diagnosis, and management

Machine learning models for the early real-time prediction of deterioration in intensive care units: a novel approach to the early identification of high-risk patients

BACKGROUND: Predictive machine learning models have made use of a variety of scoring systems to identify clinical deterioration in ICU patients. However, most of these scores include variables that are dependent on medical staff examining the patient. We present the development of a real-time prediction model using clinical variables that are digital and automatically generated for the early detection of patients at risk of deterioration. METHODS: Routine monitoring data were used in this analysis. ICU patients with at least 24 h of vital sign recordings were included. Deterioration was defined as qSOFA ≥ 2. Model development and validation were performed internally by splitting the cohort into training and test datasets and validating the results on the test dataset. Five different models were trained, tested, and compared against each other. The models were an artificial neural network (ANN), a random forest (RF), a support vector machine (SVM), a linear discriminant analysis (LDA), and a logistic regression (LR). RESULTS: In total, 7156 ICU patients were screened for inclusion in the study, which resulted in models trained from a total of 28,348 longitudinal measurements. The artificial neural network showed a superior predictive performance for deterioration, with an area under the curve of 0.81 over 0.78 (RF), 0.78 (SVM), 0.77 (LDA), and 0.76 (LR), by using only four vital parameters. The sensitivity was higher than the specificity for the artificial neural network. CONCLUSIONS: The artificial neural network, only using four automatically recorded vital signs, was best able to predict deterioration, 10 h before documentation in clinical records. This real-time prediction model has the potential to flag at-risk patients to the healthcare providers treating them, for closer monitoring and further investigation

How young radiologists use contrast media and manage adverse reactions: an international survey

Objectives: To collect real-world data about the knowledge and self-perception of young radiologists concerning the use of contrast media (CM) and the management of adverse drug reactions (ADR). Methods: A survey (29 questions) was distributed to residents and board-certified radiologists younger than 40&nbsp;years to investigate the current international situation in young radiology community regarding CM and ADRs. Descriptive statistics analysis was performed. Results: Out of 454 respondents from 48 countries (mean age: 31.7 ± 4&nbsp;years, range 25–39), 271 (59.7%) were radiology residents and 183 (40.3%) were board-certified radiologists. The majority (349, 76.5%) felt they were adequately informed regarding the use of CM. However, only 141 (31.1%) received specific training on the use of CM and 82 (18.1%) about management ADR during their residency. Although 266 (58.6%) knew safety protocols for handling ADR, 69.6% (316) lacked confidence in their ability to manage CM-induced ADRs and 95.8% (435) expressed a desire to enhance their understanding of CM use and handling of CM-induced ADRs. Nearly 300 respondents (297; 65.4%) were aware of the benefits of contrast-enhanced ultrasound, but 249 (54.8%) of participants did not perform it. The preferred CM injection strategy in CT parenchymal examination and CT angiography examination was based on patient’s lean body weight in 318 (70.0%) and 160 (35.2%), a predeterminate fixed amount in 79 (17.4%) and 116 (25.6%), iodine delivery rate in 26 (5.7%) and 122 (26.9%), and scan time in 31 (6.8%) and 56 (12.3%), respectively. Conclusion: Training in CM use and management ADR should be implemented in the training of radiology residents. Critical relevance statement: We highlight the need for improvement in the education of young radiologists regarding contrast media; more attention from residency programs and scientific societies should be focused on training about contrast media use and the management of adverse drug reactions. Key points: • This survey investigated training of young radiologists about use of contrast media and management adverse reactions. • Most young radiologists claimed they did not receive dedicated training. • An extreme heterogeneity of responses was observed about contrast media indications/contraindications and injection strategy. Graphical Abstract: (Figure presented.

EEG for good outcome prediction after cardiac arrest: a multicentre cohort study.

AIM Assess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA). METHODS Prospective observational multicentre substudy of the "Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial", also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 hours after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0-3. RESULTS 873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 hours (IQR 47-91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50-61) sensitivity and 83% (CI 80-86) specificity to predict good outcome. Presence of EEG reactivity contributed (p<0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account. CONCLUSION Nearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance