Development and validation of risk and prognostic nomograms for bone metastases in Chinese advanced colorectal cancer patients

Predictive nomogram for bone metastases in lung cancer based on monocyte infiltration

The presence of bone metastases (BM) in patients with lung cancer is indicative of a worse prognosis. The present study aims to investigate the risk factors associated with BM in patients with lung cancer. Patients with lung cancer admitted to the First Affiliated Hospital of Anhui Medical University between June 2019 and September 2021 were enrolled in this study. A nomogram was constructed based on the outcomes derived from univariate and multivariate analyses. Concordance index, calibration plots, receiver operating characteristic curves, and decision curve analysis were used to evaluate the nomogram. To substantiate the influence of monocytes on lung cancer BM, various assays, including cell co-culture, Transwell, wound-healing assays, and immunohistochemistry and immunofluorescence staining, were conducted. Statistical analyses were performed using SPSS 22.0 software and GraphPad Prism 7.0. A total of 462 eligible patients were enrolled, comprising 220 with BM and 242 without. Multivariate analysis revealed that histological type, medical history, monocyte percentage, and LDH (Lactate Dehydrogenase) and ALP (Alkaline Phosphatase) levels were independent risk factors for BM in lung cancer. Transwell and wound-healing assays indicated that co-culture with monocytes significantly enhanced the migration and invasion capabilities of A549 cells in vitro. Immunohistochemistry and immunofluorescence analyses demonstrated a noteworthy increase in monocyte infiltration in the primary lesions of patients with lung cancer with BM. In conclusion, this study successfully constructed and validated a precise, straightforward, and cost-effective prognostic nomogram for patients with lung cancer with BM.

Disease characteristics and prognostic factors of colorectal cancer patients with bone metastasis: A real-world data from Turkey

BACKGROUND

Bone metastasis is rarely seen in colorectal cancer (CRC) patients, and there is insufficient data available regarding such cases. The study aimed to identify the prognostic factors and characteristics associated with overall survival in patients with bone metastatic CRC.

METHOD

Data from bone metastatic CRC patients referred to a high-volume tertiary cancer center in Turkey, between January 2018 and April 2021, were retrospectively collected. The records of 150 consecutive patients treated for bone metastases due to CRC were reviewed. Overall survival curves were generated by the Kaplan-Meier method and analyzed using the log-rank test.

RESULTS

Median age was 55 years (19-86 years). Bone metastases were more common in men and those with metachronous metastases. The axial skeleton was the most commonly involved site, and patients were frequently presented with single bone metastasis. Peritoneal metastases were significantly correlated with extra-axial metastases (P = 0.002), and radiotherapy was applied to axial metastases significantly, more frequently (P = 0.02). Lung metastasis was also more prevalent in K-RAS mutated patients (P = 0.008). The median survival time from diagnosis of bone metastasis was 8.3 months (95% confidence interval (CI), 5.5-10.6), and the three-year survival rate was 76.9% (95% CI, 69.8-84.0). Multivariate analysis revealed that brain metastases, right-sided colon tumor, high serum ALP, and Ca 19-9 levels were independent poor prognostic factors (P = 0.01, 0.02, <0.001, and 0.04, respectively).

CONCLUSIONS

The location of CRC correlates significantly with the site of bone metastasis; the prognosis of CRC patients with bone metastasis is very poor, and the significant poor prognostic factors are brain metastases, right-sidedness, high serum ALP, and Ca 19-9 levels. More attention should be paid to bone metastasis in CRC patients.

Efficacy of mFOLFOX6 plus bevacizumab regimen in advanced colorectal cancer after deep hyperthermia: a single-center retrospective study

Background

This study aimed to explore the clinical efficacy and safety of a modified FOLFOX6 (oxaliplatin + leucovorin + 5-fluorouracil) plus bevacizumab regimen after deep hyperthermia in advanced colorectal cancer.

Methods

A total of 80 colorectal cancer patients treated at our hospital were selected as research subjects. According to the random number table method, patients were divided into a control group (mFOLFOX6 plus bevacizumab) and a combination group (mFOLFOX6 plus bevacizumab after deep hyperthermia treatment), with 40 patients in each group. After six cycles of treatment, the objective response rate (ORR), disease control rate (DCR), levels of serum tumor markers carcinoembryonic antigen (CEA), vascular epidermal growth factor (VEGF), Karnofsky performance status (KPS) scores, and the occurrence of adverse events were compared between the two groups.

Results

After six cycles of treatment, the ORR in the combination group was higher than that in the control group, but the difference was not statistically significant (P>0.05). The DCR in the combination group was significantly higher than that in the control group (P<0.05). The serum CEA levels in the control and combination groups after treatment were significantly lower than those before treatment, and the serum CEA and VEGF levels in the combination group were significantly lower than those in the control group (all P<0.001). The KPS scores in both groups after treatment were higher than those before treatment, and the KPS scores in the combination group after treatment were significantly higher than those in the control group (all P<0.001). The incidence of fatigue and pain in the combination group was significantly lower than that in the control group (P<0.05).

Conclusion

mFOLFOX6 plus bevacizumab after deep hyperthermia is effective in advanced colorectal cancer patients, which can effectively improve their quality of life, and the adverse events are controllable and tolerable. A randomized or prospective trial will be required to further prove these data and explore its potentiality, especially if compared to conventional treatment.

Prognostic impact of colorectal cancer patients with bone metastases: a single-center experience

The incidence of bone metastasis (BM) in colorectal cancer (CRC) patients is low and the prognosis is poor. There is no clear conclusion on the risk factors affecting the survival of CRC patients with BM. The aim of this study was to investigate the factors that may affect the prognosis of CRC patients with BM. The clinical and pathological data of CRC patients with BM were retrospectively analyzed. The overall survival after BM diagnosis was estimated using the Kaplan-Meier method and Log-rank test, and a multivariable cox regression model was used to identify the prognostic factors of overall survival. This study included 178 CRC patients with BM, of whom 151 had left-sided CRC and 27 had right-sided colon cancer. 1124 CRC patients with BM from the SEER database were included to perform a sensitivity analysis of the primary outcome. Multivariate analysis showed that the N staging, site of BM, and primary tumor sidedness (PTS) were independent prognostic factors for CRC with BM. Among them, right-sided colon cancer patients with BM had a poorer prognosis. Sensitivity analyses showed that PTS was an independent prognostic factor in CRC patients with BM. Primary tumor sidedness and N stage may be potential prognostic markers for BM of CRC. The prognosis of N0 stage CRC with BM is better, while the prognosis of right-sided colon cancer is poor.

Potential biomarkers for the early detection of bone metastases

The clinical manifestations of bone metastases are diversified while many sites remain asymptomatic at early stage. As the early diagnosis method is not perfect and the early symptoms of tumor bone metastasis are not typical, bone metastasis is not easy to be detected. Therefore, the search for bone metastasis-related markers is effective for timely detection of tumor bone metastases and the development of drugs to inhibit bone metastases. As a result, bone metastases can only be diagnosed when symptoms are found, increasing the risk of developing skeletal-related event (SREs), which significantly impairs the patient's quality of life. Therefore, the early diagnosis of bone metastases is of great importance for the treatment and prognosis of cancer patients. Changes of bone metabolism indexes appear earlier in bone metastases, but the traditional biochemical indexes of bone metabolism lack of specificity and could be interfered by many factors, which limits their application in the study of bone metastases. Some new biomarkers of bone metastases have good diagnostic value, such as proteins, ncRNAs, circulating tumor cells (CTCs). Therefore, this study mainly reviewed the initial diagnostic biomarkers of bone metastases which were expected to provide references for the early detection of bone metastases.

Machine learning algorithms to predict atypical metastasis of colorectal cancer patients after surgical resection

Background

The prognosis of colorectal cancer with atypical metastasis is poor. However, atypical metastasis was less common and under-appreciated.

Methods

In this study we attempted to present the first machine learning models to predict the risk of atypical metastasis in colorectal cancer patients. We evaluated the differences between metastasis and non-metastasis groups, assessed factors associated with atypical metastasis using univariate and multivariate logistic regression analyses, and preliminarily developed the multiple machine learning models to predict atypical metastasis.

Results

168 patients were included. Prognostic Nutritional Index (PNI) [OR =  0.998; P = 0.030], Cancer antigen 19-9 (CA19-9) [OR = 1.011; P = 0.043] and MR-Distance [-mid OR = 0.289; P = 0.009] [-high OR = 0.248; P = 0.021] were shown to be independent risk factors for the atypical metastasis via multivariate analysis. Furthermore, the machine learning model based on AdaBoost algorithm (AUC: 0736) has better predictive performance comparing to Logistic Regression (AUC: 0.671) and KNeighbors Classifier (AUC: 0.618) by area under the curve (AUC) in the validation cohorts. The accuracy, sensitivity, and specificity of the model trained using the Adaboost method in the validation set are 0.786, 0.776 and 0.700, while 0.601, 0.933, 0.508 using Logistic Regression and 0.743, 0.390, 0.831 using KNeighbors Classifier.

Conclusion

Machine-learning approaches containing PNI, CA19-9 and MR-Distance show great potentials in atypical metastasis prediction.

Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer

Background

This study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients.

Methods

From SEER database, 50,566 CRC patients were identified between January 2015 and December 2019 without missing data. SVM and LR models were trained and tested on the dataset. Accuracy, area under the curve (AUC), and IDI were used to evaluate and compare the models.

Results

For bone metastases in the entire cohort, SVM model with poly as kernel function presents the best performance, whose accuracy is 0.908, recall is 0.838, and AUC is 0.926, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size.

Conclusion

Our study developed an SVM model with poly as kernel function for predicting BM in CRC patients. SVM model could improve personalized clinical decision-making, help rationalize the bone metastasis screening process, and reduce the burden on healthcare systems and patients.

Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study

Background

Hepato-pulmonary metastasis of colorectal cancer (CRC) is a rare disease with poor prognosis. This study aims to establish a highly efficient nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer hepato-pulmonary metastasis (CRCHPM).

Methods

We retrospectively analyzed the data of patients with CRCHPM from SEER database and Wuhan Union Hospital Cancer Center (WUHCC). A total of 1250 CRCHPM patients were randomly assigned to the training, internal validation, and external validation cohorts from 2010 to 2016.Univariate and multivariate cox analysis were performed to identify independent clinicopathological predictors of OS and CSS, and a nomogram was constructed to predict OS and CSS in CRCHPM patients.

Results

A nomogram of OS was constructed based on seven independent predictors of age, degree of differentiation, T stage, chemotherapy, number of lsampled lymph nodes, number of positive lymph nodes, and tumor size. Nomogram showed favorable sensitivity in predicting OS at 1, 3 and 5 years, with area under the receiver operating characteristic curve (AUROC) values of 0.802, 0.759 and 0.752 in the training cohort;0.814, 0.769 and 0.716 in the internal validation cohort;0.778, 0.756 and 0.753 in the external validation cohort, respectively. A nomogram of CSS was constructed based on three independent predictors of T stage, chemotherapy, and tumor size. The AUROC values of 1, 3 and 5 years were 0.709,0.588,0.686 in the training cohort; 0.751, 0.648,0.666 in the internal validation cohort;0.781,0.588,0.645 in the external validation cohort, respectively. Calibration curves, Concordance index (C-index), and decision curve analysis (DCA) results revealed that using our model to predict OS and CSS is more efficient than other single clinicopathological characteristics.

Conclusion

A nomogram of OS and CSS based on clinicopathological characteristics can be conveniently used to predict the prognosis of CRCHPM patients.

Nomograms based on lactate dehydrogenase to albumin ratio for predicting survival in colorectal cancer

Purpose: We aimed to determine if lactate dehydrogenase to albumin ratio (LAR) might play a prognostic role for patients with operable colorectal cancer (CRC). Patients and Methods: 1334 operable CRC patients in Wuhan Union Hospital Between July 2013 and September 2017 were enrolled in this study and were randomly appointed them into training (n=954) and validation (n=380) sets. The relationship between LAR and overall survival (OS) and disease-free survival (DFS) were determined by restricted cubic splines (RCS) with Cox regression models. LAR was then divided into three categories based on the RCS and compared to the well-known TNM stage system. Finally, survival nomograms were developed by compounding the LAR and other clinical factors. Results: Baseline LAR values and the all-cause mortality were U shaped, which slowly decreased until around 4.50 and then started to increase rapidly when the LAR ranged from 4.50-6.68 and then became flat thereafter (P for non-linearity <0.001). LAR was superior to TNM stage for OS as well as DFS and LAR plus TNM stage could add more net benefit than clinical model alone. Moreover, the survival nomograms based on LAR achieved great predictive ability for OS and DFS in operable CRC patients. Conclusions: LAR could be served as a reliable prognostic factor for OS as well as DFS, with more accurate prognostic prediction than current TNM stage for patients with operable CRC.

A Clinical Prognostic Model Based on Preoperative Hematological and Clinical Parameters Predicts the Progression of Primary WHO Grade II Meningioma

Purpose

The purpose was to explore the correlation between hematological parameters and the progression of WHO grade II meningioma, and establish a clinical prognostic model based on hematological parameters and clinical prognostic factors to predict the progression-free survival (PFS) of patients.

Methods

A total of 274 patients with WHO grade II meningiomas were included. Patients were randomly divided into a training cohort (192, 70%) and a test cohort (82, 30%). In the training cohort, the least absolute shrinkage and selection operator Cox regression analysis were used to screen for hematological parameters with prognostic value, and the hematological risk model (HRM) was constructed based on these parameters; univariate and multivariate Cox regression analyses were utilized to screen for clinical prognostic factors, and a clinical prognostic model was constructed based on clinical prognostic factors and HRM. The prognostic stability and accuracy of the HRM and clinical prognostic model were verified in the test cohort. Subgroup analysis was performed according to the patients' different clinical characteristics.

Results

Preoperative neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, platelet-to-lymphocyte ratio, albumin-to-globulin ratio, D-dimer, fibrinogen, and lactate dehydrogenase were associated with the PFS of patients. The areas under curve of the HRM were 0.773 (95% confidence interval [CI] 0.707-0.839) and 0.745 (95% CI 0.637-0.852) in the training cohort and test cohort, respectively. The progression risk was higher in the high-risk group than that in the low-risk group categorized by the optimal cutoff value (2.05) of hematological risk scores. The HRM, age, tumor location, tumor size, peritumoral edema, extent of resection, Ki-67 index, and postoperative radiotherapy were the prognostic factors for the progression of meningiomas. The corrected C-index of the clinical prognosis model was 0.79 in the training cohort. Clinical decision analysis showed that the clinical prognostic model could be used to obtain favorable clinical benefits. In the subgroup analysis, the HRM displayed excellent prognostic stability and general applicability in different subgroups.

Conclusions

Preoperative hematological parameters are associated with the postoperative progression of WHO grade II meningiomas. The clinical prognosis model constructed based on hematological parameters and clinical prognostic factors has favorable predictive accuracy and clinical benefits.