Bone, muscle, and metabolic parameters predict survival in patients with synchronous bone metastases from lung cancers

Microwave ablation combined with percutaneous vertebroplasty for treating painful non-small cell lung cancer with spinal metastases under real-time temperature monitoring

PURPOSE

To retrospectively study the therapeutic effect and safety performance of the combination strategies of the computed tomography (CT)-guided microwave ablation (MWA) and percutaneous vertebroplasty (PVP) as a treatment for painful non-small cell lung cancer (NSCLC) with spinal metastases.

MATERIALS AND METHODS

A retrospective review included 71 patients with 109 vertebral metastases who underwent microwave ablation combined with percutaneous vertebroplasty by the image-guided and real-time temperature monitoring. Treatment efficacy was determined by comparing visual analog scale (VAS) scores, daily morphine equivalent opioid consumption, and Oswestry Disability Index (ODI) scores before treatment and during the follow-up period.

RESULTS

Technical success was achieved in all patients. The mean pre-procedure VAS score and morphine doses were 6.6 ± 1.8 (4-10) and 137.2 ± 38.7 (40-200) mg, respectively. The mean VAS scores and daily morphine doses at 24 h and 1, 4, 12, and 24 weeks postoperatively were 3.3 ± 1.9 and 73.5 ± 39.4 mg; 2.2 ± 1.5 and 40.2 ± 29.8 mg; 1.7 ± 1.2 and 31.3 ± 23.6 mg; 1.4 ± 1.1 and 27.3 ± 21.4 mg; and 1.3 ± 1.1 and 24.8 ± 21.0 mg, respectively (all P < 0.001). ODI scores significantly decreased (P < 0.05). Minor cement leakage occurred in 51 cases (46.8%), with one patient having a grade 3 neural injury. No local tumor progression was observed by follow-up imaging.

CONCLUSIONS

MWA combined with PVP can significantly relieve pain and improve patients' quality of life, which implied this is an effective treatment option for painful NSCLC with spinal metastases. Additionally, its efficacy should be further verified through the mid- and long-term studies.

Peripheral but not axial muscle mass is associated with early mortality in bone metastatic lung cancer patients at diagnosis

OBJECTIVES

Identification of sarcopenia is a key issue in oncology. Several methods may be used to evaluate muscle mass in patients. Routine cancer follow-up computed tomography (CT) provides axial muscle mass whereas whole-body densitometry (DEXA) measures appendicular lean mass (ALM). Up to now, no studies have assessed, in cancer patients, the correlation between CT and DEXA muscle mass indicators and compared their prognostic value.

METHODS

We included patients with synchronous bone metastases from lung adenocarcinoma at diagnosis. Diagnosis was confirmed by bone biopsy. Skeletal muscle area was determined semi-automatically on initial CT scan at the T7, T12, and L3 vertebral level using Osirix® software. The skeletal muscle index (SMI) was calculated as the ratio of muscle area to height squared. Standardised ALM/height squared data were obtained by DEXA assessment within a 30-day window of CT.

RESULTS

A total of 65 patients were included; 47 (72%) were male. Mean±SD age was 65±11.4years. DEXA was available for 46 patients. The performance status was good (<1) for 39 patients. SMI indicators were significantly correlated with each other (rho from 0.3 to 0.7) but moderately correlated with ALM (rho from 0.1 to 0.7). ALM had a good discriminatory ability on 6-month survival (AUC 0.87 [0.76; 0.97]). ALM was associated with early mortality (<6months) (HR=0.29, 95% CI [0.15; 0.57]; P<0.001) but not with later mortality (>6months). In contrast, no significant effect was found for SMI.

CONCLUSIONS

Peripheral muscle mass (standardized ALM by DEXA) but not axial muscle mass (SMI assessed by CT) was associated with early mortality (<6months) suggesting that cancer-induced muscle loss would affect differently appendicular muscles and axial muscles.

Pre-Therapeutic Sarcopenia among Cancer Patients: An Up-to-Date Meta-Analysis of Prevalence and Predictive Value during Cancer Treatment

This study will address the prevalence of pre-therapeutic sarcopenia (PS) and its clinical impact during cancer treatment among adult cancer patients ≥ 18 years of age. A meta-analysis (MA) with random-effect models was performed via a MEDLINE systematic review, according to the PRISMA statement, focusing on articles published before February 2022 that reported observational studies and clinical trials on the prevalence of PS and the following outcomes: overall survival (OS), progression-free survival (PFS), post-operative complications (POC), toxicities (TOX), and nosocomial infections (NI). A total of 65,936 patients (mean age: 45.7-85 y) with various cancer sites and extensions and various treatment modes were included. Mainly defined by CT scan-based loss of muscle mass only, the pooled prevalence of PS was 38.0%. The pooled relative risks were 1.97, 1.76, 2.70, 1.47, and 1.76 for OS, PFS, POC, TOX, and NI, respectively (moderate-to-high heterogeneity, I²: 58-85%). Consensus-based algorithm definitions of sarcopenia, integrating low muscle mass and low levels of muscular strength and/or physical performance, lowered the prevalence (22%) and heterogeneity (I² < 50%). They also increased the predictive values with RRs ranging from 2.31 (OS) to 3.52 (POC). PS among cancer patients is prevalent and strongly associated with poor outcomes during cancer treatment, especially when considering a consensus-based algorithm approach.

Effect of Cancer-Related Cachexia and Associated Changes in Nutritional Status, Inflammatory Status, and Muscle Mass on Immunotherapy Efficacy and Survival in Patients with Advanced Non-Small Cell Lung Cancer

Immune checkpoint inhibitor (ICI)-based immunotherapy has significantly improved the survival of patients with advanced non-small cell lung cancer (NSCLC); however, a significant percentage of patients do not benefit from this approach, and predictive biomarkers are needed. Increasing evidence demonstrates that cachexia, a complex syndrome driven by cancer-related chronic inflammation often encountered in patients with NSCLC, may impair the immune response and ICI efficacy. Herein, we carried out a prospective study aimed at evaluating the prognostic and predictive role of cachexia with the related changes in nutritional, metabolic, and inflammatory parameters (assessed by the multidimensional miniCASCO tool) on the survival and clinical response (i.e., disease control rate) to ICI-based immunotherapy in patients with advanced NSCLC. We included 74 consecutive patients. Upon multivariate regression analysis, we found a negative association between IL-6 levels (odds ratio (OR) = 0.9036; 95%CI = 0.8408-0.9711; p = 0.0025) and the miniCASCO score (OR = 0.9768; 95%CI = 0.9102-0.9999; p = 0.0310) with the clinical response. As for survival outcomes, multivariate COX regression analysis found that IL-6 levels and miniCASCO-based cachexia severity significantly affected PFS (hazard ratio (HR) = 1.0388; 95%CI = 1.0230-1.0548; p < 0.001 and HR = 1.2587; 95%CI = 1.0850-1.4602; p = 0.0024, respectively) and OS (HR = 1.0404; 95%CI = 1.0221-1.0589; p < 0.0001 and HR = 2.3834; 95%CI = 1.1504-4.9378; p = 0.0194, respectively). A comparison of the survival curves by Kaplan-Meier analysis showed a significantly lower OS in patients with cachexia versus those without cachexia (p = 0.0323), as well as higher miniCASCO-based cachexia severity (p = 0.0428), an mGPS of 2 versus those with a lower mGPS (p = 0.0074), and higher IL-6 levels (>6 ng/mL) versus those with lower IL-6 levels (≤6 ng/mL) (p = 0.0120). In conclusion, our study supports the evidence that cachexia, with its related changes in inflammatory, body composition, and nutritional parameters, is a key prognostic and predictive factor for ICIs. Further larger studies are needed to confirm these findings and to explore the potential benefit of counteracting cachexia to improve immunotherapy efficacy.

Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques

Background

Individualized therapeutic strategies can be carried out under the guidance of expected lifespan, hence survival prediction is important. Nonetheless, reliable survival estimation in individuals with bone metastases from cancer of unknown primary (CUP) is still scarce. The objective of the study is to construct a model as well as a web-based calculator to predict three-month mortality among bone metastasis patients with CUP using machine learning-based techniques.

Methods

This study enrolled 1010 patients from a large oncological database, the Surveillance, Epidemiology, and End Results (SEER) database, in the United States between 2010 and 2018. The entire patient population was classified into two cohorts at random: a training cohort (n=600, 60%) and a validation cohort (410, 40%). Patients from the validation cohort were used to validate models after they had been developed using the four machine learning approaches of random forest, gradient boosting machine, decision tree, and eXGBoosting machine on patients from the training cohort. In addition, 101 patients from two large teaching hospital were served as an external validation cohort. To evaluate each model's ability to predict the outcome, prediction measures such as area under the receiver operating characteristic (AUROC) curves, accuracy, and Youden index were generated. The study's risk stratification was done using the best cut-off value. The Streamlit software was used to establish a web-based calculator.

Results

The three-month mortality was 72.38% (731/1010) in the entire cohort. The multivariate analysis revealed that older age (P=0.031), lung metastasis (P=0.012), and liver metastasis (P=0.008) were risk contributors for three-month mortality, while radiation (P=0.002) and chemotherapy (P<0.001) were protective factors. The random forest model showed the highest area under curve (AUC) value (0.796, 95% CI: 0.746-0.847), the second-highest precision (0.876) and accuracy (0.778), and the highest Youden index (1.486), in comparison to the other three machine learning approaches. The AUC value was 0.748 (95% CI: 0.653-0.843) and the accuracy was 0.745, according to the external validation cohort. Based on the random forest model, a web calculator was established: https://starxueshu-codeok-main-8jv2ws.streamlitapp.com/. When compared to patients in the low-risk groups, patients in the high-risk groups had a 1.99 times higher chance of dying within three months in the internal validation cohort and a 2.37 times higher chance in the external validation cohort (Both P<0.001).

Conclusions

The random forest model has promising performance with favorable discrimination and calibration. This study suggests a web-based calculator based on the random forest model to estimate the three-month mortality among bone metastases from CUP, and it may be a helpful tool to direct clinical decision-making, inform patients about their prognosis, and facilitate therapeutic communication between patients and physicians.

Management of cancer pain due to bone metastasis

Bone metastases frequently occur in patients with cancer. Skeletal-related events (SREs), including pain, impaired mobility, hypercalcemia, pathological fracture, spinal cord and nerve root compression, and bone marrow infiltration, can decrease the quality of life of the patients and increase the risk of morbidity. The mechanism of pain due to bone metastasis is complicated and involves various interactions among tumor cells, bone cells, activated inflammatory cells, and bone-innervating neurons. Cancer pain due to bone metastasis can be crippling and a chronic state that causes sarcopenia. For pain management, it is important to diagnose whether the pain is based on background pain or breakthrough pain due to bone metastasis. In addition, the management goal of cancer pain due to bone metastasis is not only to achieve pain relief but also to prevent pain progression and SREs. Pain mechanisms should be applied to achieve optimal management. This review aims to discuss the mechanisms of cancer pain due to bone metastasis and review the recommended drug therapies.

Construction and Verification of Nomogram Model for Lung Adenocarcinoma With ≤ 5 Bone-Only Metastases Basing on Hematology Markers

Objectives

This retrospective study investigated prognostic factors in advanced lung adenocarcinoma (LUAD) with one to five bone-only metastasis (BOM) and developed a nomogram model to estimate patient survival.

Methods

We investigated patients with advanced LUAD with one to five bone-only metastasis at the initial diagnosis and diagnosed between 2013 and 2019 in two hospitals. A formula named Risk-H was constructed using hematological variables screened by LASSO-Cox regression analysis in the internal set and verified by the external set. Two nomogram models were developed by clinical variables selected by LASSO-Cox regression analysis with or without Risk-H in the internal set. The concordance index (C-index), calibration curves, time-dependent receiver operating characteristic (ROC) analysis, area under the curve (AUC), and decision curve analysis (DCA) were formulated to verify nomogram models. The primary endpoint was overall survival.

Results

We finally included 125 and 69 patients, respectively, in the internal and external sets for analysis. The following were significant hematology prognostic factors and were included in the Risk-H formula: alkaline phosphatase and albumin, leukocyte. Four clinical factors, including loss of weight, sensitive mutation status, T and N stage, with or without Risk-H were used to establish nomogram models. C-index, calibration curves, ROC analysis, AUC, and DCA showed the addition of hematological data improved the predictive accuracy of survival.

Conclusions

Pretreatment peripheral blood indexes may be a meaningful serum biomarker for prognosis in LUAD. The addition of Risk-H to the nomogram model could serve as a more economical, powerful, and practical method to predict survival for LUAD patients with one to five BOM.

Fracture Risk Evaluation of Bone Metastases: A Burning Issue

Major progress has been achieved to treat cancer patients and survival has improved considerably, even for stage-IV bone metastatic patients. Locomotive health has become a crucial issue for patient autonomy and quality of life. The centerpiece of the reflection lies in the fracture risk evaluation of bone metastasis to guide physician decision regarding physical activity, antiresorptive agent prescription, and local intervention by radiotherapy, surgery, and interventional radiology. A key mandatory step, since bone metastases may be asymptomatic and disseminated throughout the skeleton, is to identify the bone metastasis location by cartography, especially within weight-bearing bones. For every location, the fracture risk evaluation relies on qualitative approaches using imagery and scores such as Mirels and spinal instability neoplastic score (SINS). This approach, however, has important limitations and there is a need to develop new tools for bone metastatic and myeloma fracture risk evaluation. Personalized numerical simulation qCT-based imaging constitutes one of these emerging tools to assess bone tumoral strength and estimate the femoral and vertebral fracture risk. The next generation of numerical simulation and artificial intelligence will take into account multiple loadings to integrate movement and obtain conditions even closer to real-life, in order to guide patient rehabilitation and activity within a personalized-medicine approach.

Estimating survival and clinical outcome in advanced non-small cell lung cancer with bone-only metastasis using molecular markers

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Studies about prognostic factors of NSCLC patients with BOM are lacking.

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Five significant independent prognostic factors for them were found.

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We developed a GPA model to estimate life expectancy and to guide interventions.

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The more distal metastases to the spine, the worse the prognosis.

Objectives

This retrospective study investigated prognostic factors in advanced non-small cell lung cancer (NSCLC) with bone-only metastasis, and developed a graded prognostic assessment (GPA) model to estimate patient survival.

Methods

The primary endpoint was overall survival. We investigated the patients with advanced NSCLC with bone-only metastasis at the initial diagnosis and diagnosed between 2013 and 2019 in our hospital. A log-rank test and Cox proportional hazards model were used to examine factors. A GPA model was developed in the training set based on the factors that were determined significant according to their hazard ratios and verified by the validation set.

Results

We finally included 220 patients for analysis. These patients were divided into two groups, 147 cases for the training cohort and 73 for the validation cohort. The following were significant independent prognostic factors, and were included in the GPA model: smoking; EGFR (epidermal growth factor receptor) sensitive/ALK (anaplastic lymphoma kinase) mutations; loss of weight; hypoalbuminemia; and primary site treated by surgery or radiotherapy. GPA score of nil was assigned to smoking, without sensitive mutations, loss of weight, hypoalbuminemia, and without local treatment of primary site; the corresponding superior alternatives were scored 1.5, 2.0, 1.5, 1.5, and 1.5, respectively. The median survival times of patients with GPA scores of nil to 3.0, 3.5 to 6.0, and 6.5 to 8.0 were 14.2, 29.5, and 56.6 months in the training set (P < 0.001) and 15.2, 31.2, and 54.0 months in the validation set (P < 0.001).

Conclusion

The survival time of patients with NSCLC with bone-only metastasis was dramatically influenced by the presence of the determined prognostic factors. The GPA model developed in this study may be a useful clinical tool to estimate the life expectancy of these patients, and guide treatment.

Serum total periostin is an independent marker of overall survival in bone metastases of lung adenocarcinoma

More than 35% of lung adenocarcinoma patients have bone metastases at diagnosis and have a poor survival. Periostin, a carboxylated matrix protein, mediates lung cancer cell dissemination by promoting epithelial-mesenchymal transition, and is involved in bone response to mechanical stress and bone formation regulation. This suggests that periostin may be used as a biomarker to predict survival in lung cancer patients. Serum periostin was assessed at diagnosis in a prospective cohort of 133 patients with lung adenocarcinoma of all stages. Patients were divided into localized and bone metastatic groups. Both groups were matched to healthy controls. Survival analysis and Cox proportional hazards models were conducted in the total population and in bone metastatic group. The median serum periostin level was higher in bone metastatic (n = 67; median: 1752 pmol/L) than in the localized group (n = 66; 861 pmol/L; p < 0.0001). Patients with high periostin (>median) had a poorer overall survival in the whole population (33.3 weeks vs. NR; p < 0.0001) and the bone metastatic group (24.4 vs. 66.1 weeks; p < 0.001). In multivariate analysis, patients with high periostin had increased risk of death (HR = 2.09, 95%CI [1.06-4.13]; p = 0.03). This was also found in the bone metastatic group (HR = 3.62, 95%CI [1.74-7.52]; p = 0.0005). Immunohistochemistry on bone metastasis biopsies showed periostin expression in the bone matrix and nuclear and cytoplasmic staining in cancer cells. Serum periostin was an independent survival biomarker in all-stage and in bone metastatic lung adenocarcinoma patients. IHC data suggest that periostin might be induced in cancer cells in bone metastatic niche in addition to bone microenvironment expression.

Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis

This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer spinal bone metastasis were taken as research subjects, and comprehensive evaluation was made in terms of preliminary classification of images, segmentation results, Dice index, and Jaccard coefficient. After the case of misjudgment on whether there was hot spot was excluded, the initial classification accuracy of the AdaBoost algorithm can reach 96.55%. True positive rate (TPR) was 2.3%, and false negative rate (FNR) was 1.15%. 45 MRI images with hot spots were utilized as test set to detect the segmentation accuracy of CV, maximum between-cluster variance method (OTSU), and region growing algorithm. The results showed that the Dice index and Jaccard coefficient of the CV algorithm were 0.8591 and 0.8002, respectively, which were considerably superior to OTSU (0.6125 and 0.5541) and region growing algorithm (0.7293 and 0.6598). In summary, the AdaBoost algorithm was adopted for image preliminary classification, and CV algorithm for image segmentation was ideal for the diagnosis of lung cancer spinal bone metastasis and it was worthy of clinical promotion.

Impact of body composition, nutritional and inflammatory status on outcome of non-small cell lung cancer patients treated with immunotherapy

BACKGROUND AND AIMS

Body composition and balance of nutritional and inflammatory status are important for the immune system. Alterations of these aspects may impact on response, outcome and toxicities of immunotherapy. In this review we try to clarify some definitions and tools used for the assessment of the different aspects of nutritional disorders, body composition and inflammatory status with a focus on lung cancer.

METHODS

We primary investigate the definitions of malnutrition, cachexia, sarcopenia and overweight. Secondary, tools used to measure body composition, nutritional and inflammatory status, mainly in lung cancer are reviewed.

RESULTS

All these features, in the time of precision medicine may improve assessment and selection of patients, incorporating also early palliative care in standard therapy.

CONCLUSIONS

A multimodal approach based on nutrition assessment and physical exercise should be evaluated to improve aspects of the immune response against cancer and to propose the best treatment to every patient.

The prognostic impact of sarcopenia on elderly patients undergoing pulmonary resection for non-small cell lung cancer

PURPOSE

The number of elderly patients who undergo surgery is increasing, even though they are at a high risk due to a decreased physical strength. Furthermore, sarcopenia is generally associated with a poor prognosis in patients with non-small cell lung cancer (NSCLC).

METHODS

This study included NSCLC patients  ≥ 65 years old who underwent pulmonary resection in our hospital between 2012 and 2015. Sarcopenia was assessed using the psoas muscle mass index based on computed tomography at the level of the third lumbar vertebra. We elucidated the impact of sarcopenia on short- and long-term outcomes after surgery.

RESULTS

We enrolled 259 patients, including 179 with sarcopenia. Patients with sarcopenia before surgery tended to have postoperative complications (p = 0.0521), although they did not show a poor prognosis. In patients with sarcopenia, a multivariate analysis revealed that postoperative complications and the progression of sarcopenia 1 year after surgery were significant risk factors for a poor prognosis (p = 0.0169 and 0.00370, respectively).

CONCLUSIONS

The progression of sarcopenia after surgery is associated with a poor prognosis in elderly NSCLC patients with sarcopenia. A strategy to prevent postoperative progressive sarcopenia may be necessary for improving the clinical outcome of this population.

Association between body mass index and survival outcomes for cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

BACKGROUND

Immune checkpoint inhibitors (ICIs) have been increasingly applied in the treatment of several kinds of malignancies. Some clinical demographic characteristics were reported to be associated with the ICIs efficacy. The purpose of our current meta-analysis was to clearly evaluated the relationship between BMI and ICIs efficacy for cancer patients receiving immunotherapy.

METHODS

A systematic search of Pubmed, EMBASE and conference proceedings was performed to investigate the influence of BMI on ICIs efficacy. Pooled analysis for overall survival (OS), progression-free survival (PFS) and immune-related adverse effects (IRAEs) were analyzed in current study.

RESULTS

A total of 13 eligible studies comprising 5279 cancer patients treated with ICIs were included in the analysis. The pooled analysis showed there is positive association between high BMI and improved OS and PFS among patients with ICIs treatment (OS: HR = 0.62, 95% CI 0.55-0.71, P < 0.0001; I2 = 26.3%, P = 0.202); PFS: HR = 0.71, 95% CI 0.61-0.83, P < 0.0001; I2 = 0%, P = 0.591). There is no significant difference between the incidence of all grade IRAEs between obese, overweight patients and normal patients (Overweight vs Normal: pooled RR = 1.28, 95% CI 0.76- 2.18, P = 0.356; Obese vs Normal: pooled RR = 1.36, 95% CI 0.85- 2.17, P = 0.207).

CONCLUSION

An improved OS and PFS were observed in patients with high BMI after receiving ICIs treatment compared with patients of low BMI. No significant association between BMI and incidence of IRAEs was found in cancer patients after ICIs treatment.

Impact of body fat distribution and sarcopenia on the overall survival in patients with spinal metastases receiving radiotherapy treatment: a prospective cohort study

Introduction: An increasing number of patients is diagnosed with spinal metastases due to elevated cancer incidence and improved overall survival. Patients with symptomatic spinal bone metastases often receive radiotherapy with or without surgical stabilisation. Patients with a life expectancy of less than 3 months are generally deemed unfit for surgery, therefore adequate pre-treatment assessment of life expectancy is necessary. The aim of this study was to assess new factors associated with overall survival for this category of patients.Patients and methods: Patients who received radiotherapy for thoracic or lumbar spinal metastases from June 2013 to December 2016 were included in this study. The pre-treatment planning CT for radiotherapy treatment was used to assess the patient's visceral fat area, subcutaneous fat area, total muscle area and skeletal muscle density on a single transverse slice at the L3 level. The total muscle area was used to assess sarcopenia. Furthermore, data were collected on age, sex, primary tumour, Karnofsky performance score, medical history, number of bone metastases, non-bone metastases and neurological symptoms. Univariable and multivariable cox regressions were performed to determine the association between our variables of interest and the survival at 90 and 365 days.Results: A total of 310 patients was included. The median age was 67 years. Overall survival rates for 90 and 365 days were 71% and 36% respectively. For 90- and 365-day survival, the Karnofsky performance score, muscle density and primary tumour were independently significantly associated. The visceral or subcutaneous fat area and their ratio and sarcopenia were not independently associated with overall survival.Conclusions: Of the body morphology, only muscle density was statistically significant associated with overall survival after 90 and 365 days in patients with spinal bone metastases. Body fat distribution was not significantly associated with overall survival.

Bone decalcification to assess programmed cell death ligand 1 expression in bone metastases of non-small cell lung cancers

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Rapid determination of PD-L1 expression has become a necessity in lung cancer.

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The decalcification process is a critical point for bone metastasis specimens.

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EDTA or formic acid decalcification may be used for PD-L1 IHC in routine practice.

As for molecular alterations of lung adenocarcinoma, it is critical that pathologists are able to give PD-L1 expression status before first-line of treatment. The present study compared PD-L1 expression (clone 22-C3) in decalcified using EDTA or formic acid and non-decalcified lung cancer metastases bone samples. Amongst the 84 bone samples analysed for PD-L1 expression, and independently of decalcification, TPS ≥ 1% was 25.0% and ≥ 50% was 11.4%. There was no significant difference between decalcified samples (n = 45) and non-decalcified samples (n = 39) for both TPS ≥ 1% (p = 0.32) and TPS ≥ 50% (p = 1). To conclude, we confirm decalcified bone metastasis specimens may be used for PD-L1 IHC in routine practice. These results also highlight potentially interesting specificities of the bone microenvironment that should be further studied

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion: Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

Importance of imaging in knee pain

A 72-year-old woman with a prior history of stage IIIa lung adenocarcinoma was seen with complaints of knee pain, swelling, and difficulty sleeping at night for 1 month. Although mimicking osteoarthritis, patellofemoral syndrome, and iliotibial band syndrome, radiographs showed a lytic lesion suspicious for metastatic disease. The right tibial lesion was excised, saphenous neurolysis was performed, and radiation treatment and four cycles of chemotherapy were administered. This case shows the importance of early imaging in patients with knee pain of prolonged duration seen in the primary care setting.

The Relationship between Imaging-Based Body Composition Analysis and the Systemic Inflammatory Response in Patients with Cancer: A Systematic Review

Background and aim: Cancer is the second leading cause of death globally. Nutritional status (cachexia) and systemic inflammation play a significant role in predicting cancer outcome. The aim of the present review was to examine the relationship between imaging-based body composition and systemic inflammation in patients with cancer. Methods: MEDLINE, EMBASE, Cochrane Library and Google Scholar were searched up to 31 March 2019 for published articles using MESH terms cancer, body composition, systemic inflammation, Dual energy X-ray absorptiometry (DEXA), magnetic resonance imaging (MRI), ultrasound sonography (USS) and computed tomography (CT). Studies performed in adult patients with cancer describing the relationship between imaging-based body composition and measures of the systemic inflammatory response were included in this review. Results: The literature search retrieved 807 studies and 23 met the final eligibility criteria and consisted of prospective and retrospective cohort studies comprising 11,474 patients. CT was the most common imaging modality used (20 studies) and primary operable (16 studies) and colorectal cancer (10 studies) were the most commonly studied cancers. Low skeletal muscle index (SMI) and systemic inflammation were consistently associated; both had a prognostic value and this relationship between low SMI and systemic inflammation was confirmed in four longitudinal studies. There was also evidence that skeletal muscle density (SMD) and systemic inflammation were associated (9 studies). Discussion: The majority of studies examining the relationship between CT based body composition and systemic inflammation were in primary operable diseases and in patients with colorectal cancer. These studies showed that there was a consistent association between low skeletal muscle mass and the presence of a systemic inflammatory response. These findings have important implications for the definition of cancer cachexia and its treatment.

Prognostic Value of Sarcopenia in Lung Cancer: A Systematic Review and Meta-analysis

BACKGROUND

Evidence regarding the association between sarcopenia (skeletal muscle depletion) and outcomes in patients with lung cancer varies across studies. We aimed to systematically review the prognostic value of sarcopenia in lung cancer.

METHODS

We searched Ovid MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials through July 23, 2018 to identify retrospective or prospective cohort studies. We applied the Quality in Prognostic Studies instrument to assess the risk of bias in individual studies. We examined the heterogeneity and publication bias and performed subgroup analyses and sensitivity analyses.

RESULTS

We included 13 studies (1,810 participants). The pooled prevalence of sarcopenia was 43% in patients with non-small cell lung cancer (NSCLC) and 52% in patients with small cell lung cancer (SCLC). Sarcopenia was associated with a shorter overall survival (OS) in patients with lung cancer (hazard ratio [HR], 2.23; 95% CI, 1.68-2.94). This association existed for both NSCLC (HR, 2.57; 95% CI, 1.79-3.68) and SCLC (HR, 1.59; 95% CI, 1.17-2.14). Sarcopenia was an independent predictor of shorter OS in both stage I-II NSCLC (HR, 3.23; 95% CI, 1.68-6.23) and stage III-IV NSCLC (HR, 2.19; 95% CI, 1.14-4.24). However, sarcopenia was not an independent predictor of disease-free survival in patients with NSCLC (HR, 1.28; 95% CI, 0.44-3.69).

CONCLUSIONS

Sarcopenia is highly prevalent in patients with lung cancer (approximately one in two) and an important predictor of impaired OS in patients with SCLC or with different stages of NSCLC.

Clinical analysis of patients with skeletal metastasis of lung cancer

Background

Many factors influence bone metastases of lung cancer, and several studies report about survival of skeletal metastasis. However, few studies have focused on identifying the prognostic factors for skeletal metastasis of lung cancer, especially following orthopedic surgery. We conducted a retrospective analysis of the clinical characteristics of skeletal metastasis from lung cancer and discuss the prognostic factors.

Methods

We performed a medical record review of 202 patients who were diagnosed with skeletal metastasis from lung cancer. Adenocarcinoma was found in 116 patients (57.4%), squamous cell carcinoma in 29 (14.4%), small-cell lung cancer (SCLC) in 37 (18.7%), and large-cell carcinoma and other types of cancer in 20 patients (9.9%). Orthopedic surgery for skeletal metastasis was performed in 41 patients (20.3%).

Results

Lung cancer survival was 12.1 months. After diagnosis of lung cancer, skeletal metastasis was found at a mean of 2.5 months, and skeletal metastasis survival was 9.8 months. Lung cancer survival in patients younger than 60 years was 13.8 months, and lung cancer survival in patients 60 years or older was 10.8 months (p = 0.009). Skeletal metastasis survival in patients younger than 60 years was 11.0 months, and skeletal metastasis survival in patients 60 years or older was 8.8 months (p = 0.002). Mean skeletal metastasis survival with surgery was 12.6 months and without surgery was 9.1 months (p < 0.000). In the multivariate analysis of lung cancer survival, age under 60 years [HR (95% CI) 1.549 (1.122–2.139), p = 0.008], non-small cell lung cancer pathology type [HR (95% CI) 1.711 (1.157–2.532), p = 0.008], chemotherapy for skeletal metastasis [HR (95% CI) 8.064 (3.981–16.332), p < 0.000], and radiation therapy for skeletal metastasis [HR (95% CI) 1.791 (1.170–2.742), p = 0.007] were significant, independent, good prognostic factors. In the multivariate analysis of skeletal metastasis survival, age under 60 years [HR (95% CI) 1.549 (1.124–2.134), p = 0.007], non-small cell lung cancer pathology type [HR (95% CI) 2.045 (1.373–3.047), p < 0.000], chemotherapy for skeletal metastasis [HR (95% CI) 7.121 (3.542–14.317), p < 0.000], and orthopedic surgical treatment for skeletal metastasis [HR (95% CI) 1.710 (1.148–2.547), p = 0.008] were significant, independent, good prognostic factors.

Conclusions

Patients who survived longer were less than 60 years old, received chemotherapy as treatment for skeletal metastasis, had NSCLC rather than SCLC, and underwent orthopedic surgery for skeletal metastasis.

Epidermal Growth Factor Receptor Gene Mutation Status in Primary Lung Adenocarcinoma and Corresponding Bone Metastases

BACKGROUND

The aim of this study was to compare epidermal growth factor receptor (EGFR) mutations between primary tumors and corresponding bone metastases (BMs) in lung adenocarcinoma.

MATERIALS AND METHODS

In total, 115 paired primary lung adenocarcinoma and corresponding BM tumors were analyzed for EGFR mutations by Amplification Refractory Mutation System.

RESULTS

EGFR mutations were detected in 61 primary lung adenocarcinomas (53.04%) and in 67 corresponding metastases (58.26%), respectively. The EGFR mutation rate was significantly higher in female and in never-smoker patients. The consistency of EGFR mutations between the 115 matched BMs and primary tumor tissue samples reached to 80.87%, and the disparity was 19.13%. Mutations in exons 19 (19-del) and 21 (point mutation L858R) were the predominant mutation type.

CONCLUSIONS

The concordance rate demonstrated the feasibility of EGFR mutations in corresponding metastases using Amplification Refractory Mutation System when the primary tumor tissue is unavailable in the lung adenocarcinoma patients, and the inconsistency indicates that corresponding metastasis being screened simultaneously with the primary tumor samples may present some supplementary information for the patients.

Inter-tissue communication in cancer cachexia

Cachexia is a systemic condition that occurs during many neoplastic diseases, such as cancer. Cachexia in cancer is characterized by loss of body weight and muscle and by adipose tissue wasting and systemic inflammation. Cancer cachexia is often associated with anorexia and increased energy expenditure. Even though the cachectic condition severely affects skeletal muscle, a tissue that accounts for ~40% of total body weight, it represents a multi-organ syndrome that involves tissues and organs such as white adipose tissue, brown adipose tissue, bone, brain, liver, gut and heart. Indeed, evidence suggests that non-muscle tissues and organs, as well as tumour tissues, secrete soluble factors that act on skeletal muscle to promote wasting. In addition, muscle tissue also releases various factors that can interact with the metabolism of other tissues during cancer. In this Review, we examine the effect of non-muscle tissues and inter-tissue communication in cancer cachexia and discuss studies aimed at developing novel therapeutic strategies for the condition.

Bone turnover markers and novel biomarkers in lung cancer bone metastases

CONTEXT

Lung cancer still remains the leading cause of cancer-related mortality worldwide. Bone is one of preferred metastatic sites for lung cancer cells. So far, both accurate diagnosis and effective treatment of lung cancer bone metastases are difficult.

OBJECTIVE

This review aimed to evaluate roles of bone turnover markers (BTMs), microRNAs (miRNAs), dickkopf1 (DKK1) and insulin like growth factor binding protein 3 (IGFBP-3) in lung cancer bone metastases.

METHODS

We searched articles about these four biomarkers in lung cancer bone metastases mainly in PubMed.

RESULT

The levels of bone specific alkaline phosphatase (BALP), cross-linked carboxy-terminal telopeptide of type-I collagen (ICTP) and N-terminal telopeptides of type-I collagen (NTX) were reported to be significantly increased in lung cancer patients with bone metastases. ALP, NTX and bone sialoprotein were thought to be associated with prognosis of lung cancer patients with bone metastases. MiRNA-335, miRNA-33a, miRNA-21, DKK1 and IGFBP-3 were revealed to be novel biomarkers in lung cancer bone metastases.

DISCUSSION AND CONCLUSION

Current researches have revealed that BTMs, miRNAs, DKK1 and IGFBP-3 may be useful in diagnosis, prognosis evaluation or treatment of lung cancer bone metastases. More studies about these biomarkers in lung cancer bone metastases are needed.

Bone metastases from lung cancer: A paradigm for multidisciplinary onco-rheumatology management

Bone is the third metastatic site after liver and lungs. Bone metastases occur in one out of three lung cancers and are usually of osteolytic aspect. Osteolytic bone metastases are responsible of long bone and vertebral fractures leading to restricted mobility, surgery and medullar compression that severely alter quality of life and that have a huge medico-economic impact. In the recent years, Bone Metastatic Multidisciplinary Tumour Board (BM²TB) have been developed to optimize bone metastases management for each patient in harmony with oncology program. In this review, we will go through all the different aspects of bone metastases management including diagnosis and evaluation (CT scan, Tc 99m-MDP bone scan, ¹⁸FDG-PET scan and biopsy for molecular diagnosis), systemic bone treatments (zoledronic acid and denosumab) and local treatments (interventional radiology and radiotherapy). Surgical strategies will be discussed elsewhere. Based on the last 2017-Lung Cancer South East French Guidelines, we present a practical decision tree to help the physicians for decision making in order to reach a personalized locomotor strategy for every patient.