Development of a Nomogram for Predicting the Efficacy of Preoperative Chemotherapy in Osteosarcoma

Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study

Objective

To develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS.

Methods

183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings.

Results

LASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations.

Conclusion

Combined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions.

Apatinib plus chemotherapy for non-metastatic osteosarcoma: a retrospective cohort study

Background

Effective adjuvant therapy for osteosarcoma is necessary for improved outcomes. Previous studies demonstrated that apatinib plus doxorubicin-based chemotherapy may improve the efficacy of neoadjuvant therapy. This study aimed to clarify the effectiveness and safety of apatinib plus doxorubicin and cisplatin (AP) as neoadjuvant therapy for osteosarcoma.

Methods

The clinical data of osteosarcoma patients who underwent neoadjuvant therapy and surgery between August 2016 and April 2022 were retrospectively collected and analyzed. Patients were divided into two groups: the apatinib plus AP (apatinib + AP) group and the methotrexate, doxorubicin, and cisplatin (MAP) group.

Results

This study included 42 patients with nonmetastatic osteosarcoma (19 and 23 patients in the apatinib + AP and MAP groups, respectively). The 1- and 2-year disease-free survival rates in the apatinib + AP group were higher than those in the MAP group, but the difference was not significant (P=0.165 and 0.283, respectively). Some adverse events were significantly more common in the apatinib + AP group than in the MAP group, including oral mucositis (grades 3 and 4) (52.6% vs. 17.4%, respectively, P=0.023), limb edema (47.4% vs. 17.4%, respectively, P=0.049), hand-foot syndrome (31.6% vs. 0%, respectively, P=0.005), proteinuria (26.3% vs. 0%, respectively, P=0.014), hypertension (21.1% vs. 0%, respectively, P=0.035), and hypothyroidism (21.1% vs. 0%, respectively, P=0.035). No drug-related deaths occurred. There was no statistically significant difference in the incidence of postoperative complications between the groups (P>0.05).

Conclusion

The present study suggests that apatinib + AP may be a promising candidate for neoadjuvant therapy for osteosarcoma, warranting further validation in prospective randomized controlled clinical trials with long-term follow-up.

The fibrinogen-albumin ratio as a novel prognostic factor for elderly patients with osteosarcoma

To analyze the prognostic value of fibrinogen-albumin ratio (FAR) in predicting the overall survival in elderly osteosarcoma patients. One hundred nineteen elderly osteosarcoma patients (> 40 years old) from 2 centers were retrospectively reviewed and analyzed. The cutoff values of the biomarker were calculated via receiver operating characteristic curves, and the cohort was divided into high FAR group and low FAR group. The association between the FAR and clinical-pathological parameters was analyzed. And the prognosis of elderly osteosarcoma patients and the potential risk factors were analyzed using Kaplan-Meier method and Cox proportional hazards model. Finally, a clinical nomogram was constructed, and its predictive capacity was verified. According to receiver operating characteristic results, the cutoff value for FAR was 0.098, and the enrolled patients were divided into the low FAR group and high FAR group. The FAR was significantly correlated with several clinical-pathological characteristics, including age, tumor size, tumor stage, recurrence, and metastasis. Moreover, the multivariate Cox analyses results showed that the FAR, pathological fracture, and metastasis were independent risk factors for overall survival in elderly osteosarcoma patients. The predictive nomogram was subsequently constructed, representing satisfactory predictive performance for prognosis in elderly patients with osteosarcoma. The FAR value is a promising indicator for elderly osteosarcoma patients, which is correlated with the various clinical characteristics and prognosis. A clinical nomogram integrating FAR and other clinical indicators is a convenient and available tool to assess the prognosis and manage the individualized and precise treatment of elderly patients with osteosarcoma.

The Prognostic Value of Serum Biomarkers for Survival of Children with Osteosarcoma of the Extremities

BACKGROUND

Osteosarcoma is a highly aggressive malignant bone tumor that affects mainly adolescents and young adults. We analyzed serum biomarkers for their prognostic significance in children with osteosarcoma.

METHODS

In this retrospective study, we investigated the prognostic factors in 210 children who were treated for appendicular osteosarcoma, including patient age and sex, tumor site and size (≥8 cm or <8 cm), presence of metastasis, chemotherapy-induced tumor necrosis, serum levels of alkaline phosphatase (AP), C-reactive protein, serum hemoglobin, lactate dehydrogenase, erythrocyte sedimentation rate (ESR), leukocyte counts, platelet count, and neutrophil-lymphocyte ratio.

RESULTS

A multivariate Cox regression model showed that high level of AP [HR of 1.73; 95% CI, 1.02 to 2.94], poor chemotherapy-induced tumor necrosis [HR of 2.40; 95% CI, 1.41 to 4.08] and presence of metastases at presentation [HR of 3.71; 95% CI, 2.19 to 6.29] were associated with poor prognosis at 5 years (p < 0.05). Inadequate surgical margins [HR 11.28; 95% CI, 1.37 to 92.79] and high levels of ESR [HR 3.58; 95% CI, 1.29 to 9.98] showed a greater risk of local recurrence at 5 years follow-up (p < 0.05).

CONCLUSIONS

AP and ESR can identify osteosarcoma-diagnosed children with a greater risk of death and local recurrence, respectively.

Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma

OBJECTIVES

Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years.

METHODS

486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.

RESULTS

The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (t_radscore = -5.829, χ²_ALP = 97.137, t_size = -3.437, P < 0.01). Multivariable LR analyses showed that ALP was an important indicator for predicting LM of OS (odds ratio [OR] = 7.272, P < 0.001). Among the eight models, the SVM-based clinical-radiomics model had the best performance in the validation set (AUC = 0.807, ACC = 0.784).

CONCLUSION

The clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making.

Predicting Surgical Site Infection Risk after Spinal Tuberculosis Surgery: Development and Validation of a Nomogram

Background:

The purpose of this study was to predict the surgical site infection risk after spinal tuberculosis surgery based on a nomogram.

Patients and Methods:

We collected the clinical data of patients who underwent spinal tuberculosis surgery in our hospital and included all the data in the least absolute shrinkage and selection operator (LASSO) regression analysis. Next, the selected parameters were analyzed using logistic regression. The logistic regression analysis and receiver operating characteristic (ROC) curve analysis were further used to obtain statistically significant parameters. These parameters were then used to construct a nomogram. The C-index, ROC curve, and decision curve analysis (DCA) were used to assess the predictive ability and accuracy of the nomogram, whereas internal verification was used to calculate the C-index by bootstrapping with 1,000 resamples.

Results:

A total of 394 patients with spinal tuberculosis surgery were included in the study, of whom 76 patients had surgical site infections whereas 318 patients did not. The predicted risk of surgical site infection in the nomogram ranged between 0.01 and 0.98. Both the value of the C-index of the nomogram (95% confidence interval [CI], 0.62–0.76) and the area under the curve (AUC) were found to be 0.69. The net benefit of the model ranged between 0.01 and 0.99. In contrast, the C-index calculated by the internal verification method of the nomogram was found to be 0.68.

Conclusions:

The risk factors predicting surgical site infection after spinal tuberculosis surgery included albumin, lesion segment, operation time, and incision length.