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

Confavreux, Cyrille; Pialat, Jean‐Baptiste; Bellière, A.; Brevet, Marie; Decroisette, C.; Tescaru, Agnès; Wegrzyn, Julien; Barrey, Cédric; Mornex, F.; Souquet, Pierre-Jean; Girard, Nicolas

doi:10.1016/j.jbspin.2018.03.005

Cited by 26 publications

(18 citation statements)

References 68 publications

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“…Most of the studies were published in early years. The survival of patients with brain and bone metastasis has improved steadily these years, and may be partly attributed to the early diagnoses of metastatic lesions and new treatment strategies 4, 32, 33, such as increasing use of stereotactic radiosurgery alone for patients with limited brain metastases instead of whole-brain radiotherapy 32. Third, patients diagnosed with liver metastasis had the worst survival in those with isolated organ metastasis in our study, which was consistent with previously studies 21, 28.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of site-specific metastases in lung cancer: A population based study

Hui

Sun

et al. 2019

J. Cancer

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Background : Studies on prognosis of different metastasis sites in patients with lung cancer are limited. The aim of present study was to investigate the prognostic value of metastases sites among patients with metastatic lung cancer. Methods : Between 2010 and 2014, patients diagnosed with metastatic lung cancer were selected using the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier methods were adopted and multivariable Cox regression models were built to compare the prognosis of different metastasis sites. Results : A total of 54,697 eligible patients were identified, including 10,945 (20.0%) patients had isolated bone metastases, 8,294(15.2%) with isolated brain metastases, 5,677(10.4%) with isolated liver metastases, 9,430(17.2%) with isolate lung metastases, and 20,351(37.2%) with multiple organ metastases. The percentage of bone, brain, liver, lung and multisite metastases were 22.3%, 15.4%, 6.1%, 20.1% and 36.1% for non-small cell lung cancer (NSCLC), 12.5%, 14.3%, 24.3%, 7.9%, and 40.9% for small cell lung cancer (SCLC), the difference was statistical(P<0.001). In univariate and multivariable analysis, patients with liver metastases demonstrated a statistically significant disadvantage in cause-specific survival, while those with lung metastases have reduced risk of died of metastases when compared with brain metastases(P<0.001).The difference was consistent when make subgroup analysis in both NSCLC and SCLC(P<0.001). Conclusions : In patients with distant metastases, those with liver metastases have the poorest survival, whereas those with lung metastases have the best survival. Therefore, we should take into consideration of such discrepancy when making treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Prognostic value of site-specific metastases in lung cancer: A population based study

Hui

Sun

et al. 2019

J. Cancer

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“…These methods have many limitations, such as low sensitivity, which can only be visualized when the bone damage reaches a certain severity; the equipment is expensive; the detection will cause radiation damage to the patient, etc. It is necessary to study some new methods for diagnosis of bone metastasis in lung cancer [15,16]. In recent years, serological markers and some molecular indicators related to bone metabolism have gradually been applied to the diagnosis and treatment of bone metastasis, including bone metastasis in lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Clinical significance of expression level of CX3CL1–CX3CR1 axis in bone metastasis of lung cancer

Liu

Ma²,

Dong

et al. 2020

Clin Transl Oncol

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Background: To investigate the clinical signi cance of CX3 chemokine ligand 1(CX3CL1) and CX3CR1 in patients with bone metastasis from lung cancer. The expression levels of CX3CL1 and CX3CR1 mRNA and protein in primary lung cancer and lung cancer bone metastasis were detected by qRT-PCR and Western blot. Methods: 100 patients with lung cancer were divided into a boneless metastasis group (50 patients with bone metastasis) and a bone metastasis group (50 patients without distant metastasis). The bone transfer component was graded by Soloway classi cation (0 to III). The expression levels of serum CX3CL1-CX3CR1 axis was detected by enzyme-linked immunosorbent assay (ELISA). RT-qPCR and Western Blot were used to verify the transfection e ciency. The scratching assay was used to detect the migration of CX3CL1 to 95-D cells after down-regulating the expression of CX3CR1. Results: The expression levels of CX3CL1 and CX3CR1 mRNA and protein in the primary lung cancer and lung cancer bone metastasis were signi cantly higher than those in the adjacent tissues (P<0.0001). The levels of serum CX3CL1 and CX3CR1 in bone metastasis group were signi cantly higher than those in boneless metastasis group and healthy control group (P<0.05). In the bone metastasis group, the levels of serum CX3CL1 and CX3CR1 was signi cantly positively correlated with the degree of disease progression (P<0.01). Conclusions: The expression level of serum CX3CL1-CX3CR1 axis is expected to be an auxiliary reference index for monitoring bone metastasis of lung cancer.

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“…However, abnormalities found in bone scans include not only cancer but also other bone abnormalities that can be considered benign. A malignant lesion is characterized as a cluster of dangerous tumor cells that can lead to bone cancer metastases [7]. To judge whether a lesion is malignant, the nuclear medicine physician must consider criteria such as pixel intensity (which reflects the level of radioactive uptake), lesion location, number of lesions, etc.…”

Section: Introductionmentioning

confidence: 99%

MaligNet: Semisupervised Learning for Bone Lesion Instance Segmentation Using Bone Scintigraphy

et al. 2020

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One challenge in applying deep learning to medical imaging is the lack of labeled data. Although large amounts of clinical data are available, acquiring labeled image data is difficult, especially for bone scintigraphy (i.e., 2D bone imaging) images. Bone scintigraphy images are generally noisy, and ground-truth or gold standard information from surgical or pathological reports may not be available. We propose a novel neural network model that can segment abnormal hotspots and classify bone cancer metastases in the chest area in a semisupervised manner. Our proposed model, called MaligNet, is an instance segmentation model that incorporates ladder networks to harness both labeled and unlabeled data. Unlike deep learning segmentation models that classify each instance independently, MaligNet utilizes global information via an additional connection from the core network. To evaluate the performance of our model, we created a dataset for bone lesion instance segmentation using labeled and unlabeled example data from 544 and 9,280 patients, respectively. Our proposed model achieved mean precision, mean sensitivity, and mean F1-score of 0.852, 0.856, and 0.848, respectively, and outperformed the baseline mask region-based convolutional neural network (Mask R-CNN) by 3.92%. Further analysis showed that incorporating global information also helps the model classify specific instances that require information from other regions. On the metastasis classification task, our model achieves a sensitivity of 0.657 and a specificity of 0.857, demonstrating its great potential for automated diagnosis using bone scintigraphy in clinical practice.

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

Cited by 26 publications

References 68 publications

Prognostic value of site-specific metastases in lung cancer: A population based study

Prognostic value of site-specific metastases in lung cancer: A population based study

Clinical significance of expression level of CX3CL1–CX3CR1 axis in bone metastasis of lung cancer

MaligNet: Semisupervised Learning for Bone Lesion Instance Segmentation Using Bone Scintigraphy

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