Referral patterns, treatment and outcome of high-grade malignant bone sarcoma in Scandinavia--SSG Central Register 25 years' experience

Investigation of the Incidence and Geographic Distribution of Bone and Soft Tissue Sarcomas in Canada: A National Population-Based Study

Sarcomas are a heterogeneous group of mesenchymal malignancies with various genetic and environmental risk factors. This study analyzed the epidemiology of sarcomas to gain insight into the incidence and mortality rates of these cancers in Canada, as well as to elucidate their potential environmental risk factors. Data for this study were obtained from le Registre Québécois du Cancer (LRQC) and from the Canadian Cancer Registry (CCR) for the period from 1992 to 2010. Mortality data were obtained from the Canadian Vital Statistics (CVS) database for the period from 1992 to 2010 using the International Classification of Diseases for Oncology, ICD-O-3, ICD-9, or ICD-10 codes, for all subtypes of sarcomas. We found that the overall sarcoma incidence in Canada decreased during the study period. However, there were select subtypes with increasing incidence. Peripherally located sarcomas were found to have lower mortality rates compared to axially located sarcomas, as expected. Clustering of Kaposi sarcoma cases in self-identified LGBTQ+ communities and in postal codes with a higher proportion of African-Canadian and Hispanic populations was observed. Forward Sortation Area (FSA) postal codes with a lower socioeconomic status also had higher Kaposi sarcoma incidence rates.

Prognostic Nomogram of Osteocarcinoma after Surgical Treatment

Purpose

This study aimed to establish a valid prognostic nomogram for osteocarcinoma after surgical management.

Methods

Based on the SEER database, we retrieved the clinical variables of patients confirmed to have osteocarcinoma between 1975 and 2016. Then, we performed univariate and multivariate analyses and constructed a nomogram of overall survival.

Results

Multivariate analysis of the primary cohort revealed that the independent factors for survival were age, grade, pathologic stage, T stage, and surgery performed. All these factors were showed by the nomogram. The correction curve of survival probability showed that the prediction results of nomogram well agreed with the actual observation results. The C index of the nomogram used to predict survival was 0.82; the AUC of 1-year, 3-year, and 5-year survival rates in the training cohort were 0.9, 0.819, and 0.80631, respectively, indicating that the model was accurate and reliable; whether the operation was performed or not; T stage; grade; and age were the main factors affecting the survival of patients. The AUC of the validation cohort for 1 year, 3 years, and 5 years were 0.8, 0.831, and 0.80023, respectively.

Conclusion

The proposed nomogram can more accurately predict the prognosis of patients with osteocarcinoma after surgical management. This could be a potential method that services clinical work.

A Novel Tool to Predict the Overall Survival of High-Grade Osteosarcoma Patients after Neoadjuvant Chemotherapy: A Large Population-Based Cohort Study

Background. The goal of this study was to discover clinical factors linked to overall survival in patients with high-grade osteosarcoma who had received neoadjuvant therapy and to develop a prognostic nomogram and risk classification system. Methods. A total of 762 patients with high-grade osteosarcoma were included in this study. In the training cohort, Cox regression analysis models were used to find prognostic variables that were independently linked with overall survival. To predict overall survival at 3, 5, and 8 years, a nomogram is created. In addition, in both the internal and external validation cohorts, receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were utilized to assess the prediction model’s performance. Results. The age, size of the tumor, and the stage of the disease are all important predictive variables for overall survival. The training and validation cohorts have C-indexes of 0.699 and 0.669, respectively. At the same time, the area under the curve values for both cohorts also showed that the nomogram had good discriminatory power. The calibration curve demonstrated the good performance and predictive accuracy of the model. The DCA results suggest that the nomogram has a wide range of therapeutic applications. Furthermore, a new risk classification system based on the nomogram was established, which allows all patients to be classified into three subgroups as high, middle, and low risk of death. Conclusion. The prognostic nomogram constructed in this study may provide a better precise prognostic prediction for patients with high-grade osteosarcoma after neoadjuvant chemotherapy.

Differences in health care experiences between rare cancer and common cancer patients: results from a national cross-sectional survey

Background

Patients with rare cancers face challenges in the diagnostic and treatment phase, and in access to clinical expertise. Since studies on health care experiences of these patients in comparison to patients with more common cancers are scarce, we aimed to explore these differences.

Methods

Data were cross-sectionally collected among (former) adult cancer patients through a national online survey in the Netherlands (October 2019). Descriptive statistics were reported and subgroups (rare vs. common patients) were compared.

Results

In total, 7343 patients (i.e., 1856 rare and 5487 common cancer patients) participated. Rare cancer patients were more often diagnosed and treated in different hospitals compared to common cancer patients (67% vs. 59%, p < 0.001). Rare cancer patients received treatment more often in a single hospital (60% vs. 57%, p = 0.014), but reported more negative experiences when treated in multiple hospitals than common cancer patients (14% vs. 9%, p < 0.001). They also more often received advise from their physician about the hospital to go to for a second opinion (50% vs. 36%, p < 0.001), were more likely to choose a hospital specialized in their cancer type (33% vs. 22%, p < 0.001), and were more willing to travel as long as necessary to receive specialized care than common cancer patients (55% vs. 47%, p < 0.001).

Conclusions

Rare and common cancer patients differ in their health care experiences. Health care for rare cancer patients can be further improved by proper referral to centers of expertise and building a clinical network specifically for rare cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-01886-2.

Prediction of neoadjuvant chemotherapy response in high-grade osteosarcoma: added value of non-tumorous bone radiomics using CT images

Background

This study aimed to determine the impact of including radiomics analysis of non-tumorous bone region of interest in improving the performance of pathological response prediction to chemotherapy in high-grade osteosarcomas (HOS), compared to radiomics analysis of tumor region alone.

Methods

This retrospective study included 157 patients diagnosed with HOS between November 2013 and November 2017 (age range, 5-44 years; mean age, 16.99 ±7.42 years), in which 69 and 88 patients were diagnosed as pathological good response (pGR) and non-pGR, respectively. Radiomics features were extracted from tumor and non-tumorous bone regions based on diagnostic CT images. Pathological response classifiers were developed and validated via leave-one-out cross validation (LOOCV) and independent validation methods by using the area under the receiver operating characteristic curve (AUC) value as the figure of merit.

Results

Using the LOOCV, the classifiers combining features from tumor and non-tumorous regions showed better prediction performance than those from tumor region alone (AUC, 0.8207±0.0043 vs. 0.7799±0.0044). The combined classifier also showed better performance than the tumor feature-based classifier in both training and validation datasets [training dataset: 0.791, 95% confidence interval (CI), 0.706-0.860 vs. 0.766, 95% CI, 0.679-0.840; validation dataset: 0.816, 95% CI, 0.662-0.920 vs. 0.766, 95% CI, 0.606-0.885].

Conclusions

Radiomics analysis of combined tumor and non-tumorous bone features showed improved performance of pathological response prediction to chemotherapy in HOS compared to that of tumor features alone. Moreover, the proposed classifier had the potential to predict pathological response to chemotherapy for HOS patients.

The role of tumor-associated macrophages in osteosarcoma progression - therapeutic implications

BACKGROUND

Osteosarcoma (OS) is the most common primary malignant bone tumor. Compared with previous treatment modalities, such as amputation, more recent comprehensive treatment modalities based on neoadjuvant chemotherapy combined with limb salvage surgery have improved the survival rates of patients. Osteosarcoma treatment has, however, not further improved in recent years. Therefore, attention has shifted to the tumor microenvironment (TME) in which osteosarcoma cells are embedded. Therapeutic targets in the TME may be key to improving osteosarcoma treatment. Tumor-associated macrophages (TAMs) are the most common immune cells within the TME. TAMs in osteosarcoma may account for over 50% of the immune cells, and may play important roles in tumorigenesis, angiogenesis, immunosuppression, drug resistance and metastasis. Knowledge on the role of TAMs in the development, progression and treatment of osteosarcoma is gradually improving, although different or even opposing opinions still remain.

CONCLUSIONS

TAMs may participate in the malignant progression of osteosarcoma through self-polarization, the promotion of blood vessel and lymphatic vessel formation, immunosuppression, and drug resistance. Besides, various immune checkpoint proteins expressed on the surface of TAMs, such as PD-1 and CD47, provide the possibility of the application of immune checkpoint inhibitors. Several clinical trials have been carried out and/or are in progress. Mifamotide and the immune checkpoint inhibitor Camrelizumab were both found to be effective in prolonging progression-free survival. Thus, TAMs may serve as attractive therapeutic targets. Targeting TAMs as a complementary therapy is expected to improve the prognosis of osteosarcoma. Further efforts may be made to identify potential beneficiaries of TAM-targeted therapies.

Time to Treatment Initiation and Survival in Adult Localized High-Grade Bone Sarcoma

Objective

Few studies have evaluated the prognostic implication of the length of time from diagnosis to treatment initiation in bone sarcoma. The purpose of this study is to determine if time to treatment initiation (TTI) influences overall survival in adults diagnosed with primary bone sarcoma.

Methods

A retrospective analysis of the National Cancer Database identified 2,122 patients who met inclusion criteria with localized, high-grade bone sarcoma diagnosed between 2004 and 2012. TTI was defined as length of time in days from diagnosis to initiation of treatment. Patient, disease-specific, and healthcare-related factors were also assessed for their association with overall survival. Kruskal-Wallis analysis was utilized for univariate analysis, and Cox regression modeling identified covariates associated with overall survival.

Results

Any 10-day increase in TTI was not associated with decreased overall survival (hazard ratio (HR) = 1.00; P=0.72). No differences in survival were detected at 1 year, 5 years, and 10 years, when comparing patients with TTI = 14, 30, 60, 90, and 150 days. Decreased survival was significantly associated (P < 0.05) with patient ages of 51–70 years (HR = 1.66; P=0.004) and > 71 years (HR = 2.89; P < 0.001), Charlson/Deyo score ≥2 (HR = 2.02; P < 0.001), pelvic tumor site (HR = 1.58; P < 0.001), tumor size >8 cm (HR = 1.52; P < 0.001), radiation (HR = 1.81; P < 0.001) as index treatment, and residing a distance of 51–100 miles from the treatment center (HR = 1.30; P=0.012). Increased survival was significantly associated (P < 0.05) with chordoma (HR = 0.27; P=0.010), chondrosarcoma (HR = 0.75; P=0.002), treatment at an academic center (HR = 0.64; P=0.039), and a private (HR = 0.67; P=0.006) or Medicare (HR = 0.71; P=0.043) insurer. A transition in care was not associated with a survival disadvantage (HR = 0.90; P=0.14).

Conclusions

Longer TTI was not associated with decreased overall survival in localized, high-grade primary bone sarcoma in adults. This is important in counseling patients, who may delay treatment to receive a second opinion or seek referral to a higher volume sarcoma center.

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

Background

The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination.

Methods

A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA).

Results

The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group (P < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871 (95% CI, 0.804 to 0.923) in the training cohort, and 0.843 (95% CI, 0.718 to 0.927) in the validation cohort. The DCA confirmed the clinical utility of the radiomics model.

Conclusion

The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans.

Survival Prediction in High-grade Osteosarcoma Using Radiomics of Diagnostic Computed Tomography

The poor 5-year survival rate in high-grade osteosarcoma (HOS) has not been increased significantly over the past 30 years. This work aimed to develop a radiomics nomogram for survival prediction at the time of diagnosis in HOS. In this retrospective study, an initial cohort of 102 HOS patients, diagnosed from January 2008 to March 2011, was used as the training cohort. Radiomics features were extracted from the pretreatment diagnostic computed tomography images. A radiomics signature was constructed with the lasso algorithm; then, a radiomics score was calculated to reflect survival probability by using the radiomics signature for each patient. A radiomics nomogram was developed by incorporating the radiomics score and clinical factors. A clinical model was constructed by using clinical factors only. The models were validated in an independent cohort comprising 48 patients diagnosed from April 2011 to April 2012. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Kaplan-Meier survival analysis was performed. The radiomics nomogram showed better calibration and classification capacity than the clinical model with AUC 0.86 vs. 0.79 for the training cohort, and 0.84 vs. 0.73 for the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. A significant difference (p-value <.05; log-rank test) was observed between the survival curves of the nomogram-predicted survival and non-survival groups. The radiomics nomogram may assist clinicians in tailoring appropriate therapy.

Survival and prognostic factors at time of diagnosis in high-grade appendicular osteosarcoma: a 21 year single institution evaluation from east Denmark

BACKGROUND

Survival of patients with high-grade osteosarcoma (HOS), the most common primary bone cancer, has not improved significantly the last 30 years and the disease remains a major challenge. The purpose of this study is to evaluate survival in relation to prognostic factors at time of diagnosis among patients diagnosed with primary appendicular HOS in East Denmark between 1990 and 2010.

MATERIAL AND METHODS

101 patients (median age = 20 years, female/male ratio = 56/45) diagnosed with primary appendicular high-grade osteosarcoma between 1990 and 2010 were included in this study. Initially, 156 patients diagnosed with osteosarcoma between 1990 and 2010 were identified through the population based Regional Database of Pathology, which covers a population of approximately 2.7 million (east Denmark). 55 patients were excluded due to (A) tumor was low grade (n = 22), (B) located in axial skeleton (n = 18), (C) incorrect diagnosis (n = 11) or (D) biopsy represented a tumor relapse from a former primary osteosarcoma (n = 4). Overall survival was evaluated using the Kaplan-Meier survival analysis and log-rank test. Prognostic factors were analyzed using uni- and multivariate cox-regression method with variables scored equally in the model. p Values <.05 were considered statistically significant.

RESULTS

The probability of 5- and 10-year survival was 51% (95% CI: 41-61) and 46% (95% CI: 36-56), respectively. Metastatic stage at diagnosis and tumor size ≥10 cm measured radiologically at the largest diameter were independent prognostic factors for decreased survival with significant increased hazard-risks of 3.5 (95% CI: 1.9-6.5) and 1.97 (95% CI: 1.1-3.6), respectively.

DISCUSSION

In this single institution evaluation of primary appendicular HOS we found 5-and 10-year survival rates consistent with international standards for this patient group. Distant metastases and tumor size ≥10 cm at the time of diagnosis were independent prognostic factors for decreased survival in our cohort. These results underline the importance of awareness and early referral from the primary sector.