Prognostic significance of radiodensity-based skeletal muscle quantification using preoperative CT in resected non-small cell lung cancer

Downregulation of PGC-1α during cisplatin-induced muscle atrophy in murine skeletal muscle

This study aimed to investigate the effects of cisplatin on adenosine triphosphate (ATP) levels, expressions of genes related to mitochondrial oxidative phosphorylation (OXPHOS), and the factors related to mitochondrial biosynthesis in skeletal muscle. Systemic cisplatin administration decreased skeletal muscle mass, skeletal muscle strength, and endurance. The mitochondrial DNA /nuclear DNA ratio was also reduced after treatment with cisplatin. Moreover, among the factors related to mitochondrial biogenesis and function, peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) was significantly downregulated in the cisplatin-treated group. Downregulation of PGC-1α in the skeletal muscle may contribute to muscle weakness during cisplatin-induced muscle atrophy.

Evaluation of the relationship of the T and M stage with the erector spinae muscle area in male lung cancer patients

OBJECTIVES

Sarcopenia is very common due to cachexia and presents with a decrease in skeletal muscle mass. In this study, we aimed to investigate the relationship between the T, M category and the erector spinae muscle area (ESMa).

MATERIAL AND METHODS

The initial first thorax and high-resolution computed tomography (CT) of patients with lung cancer between 2015 and 2019 were retrospectively screened. After exclusion criterias remaining 226 male patients constituted the study group. ESMa was measured manually in the section taken from the T12 vertebra spinous process level as previously described in the literature and its relationship with the T and M stage were evaluated.

RESULTS

The mean ages of patients were 70 ± 9.57 years. The T stage was T1 in 34 (15%) patients, T2 in 46 (20.4%), T3 in 59 (26.1%), and T4 in 87 (38.5%). Metastasis was detected in 83 (36.7%) patients. The mean ESMa of the patients was 34.15 ± 7.21 mm² and did not differ according to the T stage (p = .39). ESMa was lower in the metastatic group (mean 30.42 ± 6.38 mm²) than the non-metastatic group (mean 36.32 ± 6.78 mm²) (p = .0001).

CONCLUSIONS

ESMa, one of the indicators of sarcopenia, is lower in patients with metastatic lung cancer than in nonmetastatic.

Skeletal muscle index is associated with long term outcomes after lobectomy for non-small cell lung cancer

BACKGROUND

Skeletal muscle indices have been associated with improved peri-operative outcomes after surgical resection of non-small-cell lung cancer (NSCLC). However, it is unclear if these indices can predict long term cancer specific outcomes.

METHODS

NSCLC patients undergoing lobectomy at our institute between 2009-2015 were included in this analysis (N = 492). Preoperative CT scans were used to quantify skeletal muscle index (SMI) at L4 using sliceOmatic software. Cox proportional modelling was performed for overall (OS) and recurrence free survival (RFS).

RESULTS

For all patients, median SMI was 45.7 cm2/m2 (IQR, 40-53.8). SMI was negatively associated with age (R = -0.2; p < 0.05) and positively associated with BMI (R = 0.46; P < 0.05). No association with either OS or RFS was seen with univariate cox modelling. However, multivariable modelling for SMI with patient age, gender, race, smoking status, DLCO and FEV1 (% predicted), American Society of Anesthesiology (ASA) score, tumor histology and stage, and postoperative neoadjuvant therapy showed improved OS (HR = 0.97; P = 0.0005) and RFS (HR = 0.97; P = 0.01) with SMI. Using sex specific median SMI as cutoff, a lower SMI was associated with poor OS (HR = 1.65, P = 0.001) and RFS (HR = 1.47, P = 0.03).

CONCLUSIONS

SMI is associated with improved outcomes after resection of NSCLC. Further studies are needed to understand the biological basis of this observation. This study provides additional rationale for designing and implementation of rehabilitation trials after surgical resection, to gain durable oncologic benefit.

Platinum-based anticancer drugs-induced downregulation of myosin heavy chain isoforms in skeletal muscle of mouse

Cisplatin, a platinum-based anticancer drug used frequently in cancer treatment, causes skeletal muscle atrophy. It was predicted that the proteolytic pathway is enhanced as the mechanism of this atrophy. Therefore, we investigated whether a platinum-based anticancer drug affects the expression of the major proteins of skeletal muscle, myosin heavy chain (MyHC). Mice were injected with cisplatin or oxaliplatin for four consecutive days. C2C12 myotubes were treated using cisplatin and oxaliplatin. Administration of platinum-based anticancer drug reduced quadriceps mass and muscle strength compared to the control group. Protein levels of all MyHC isoforms were reduced in the platinum-based anticancer drug groups. However, only Myh2 (MyHC-IIa) gene expression in skeletal muscle of mice treated with platinum-based anticancer drugs was found to be reduced. Treatment of C2C12 myotubes with platinum-based anticancer drugs reduced the protein levels of all MyHCs, and treatment with the proteasome inhibitor MG-132 restored this reduction. The expression of Mef2c, which was predicted to act upstream of Myh2, was reduced in the skeletal muscle of mice treated systemically with platinum-based anticancer drug. Degradation of skeletal muscle MyHCs by proteasomes may be a factor that plays an important role in muscle mass loss in platinum-based anticancer drug-induced muscle atrophy.

Estimating 3-D whole-body composition from a chest CT scan

BACKGROUND

Estimating whole-body composition from limited region-computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging.

PURPOSE

To investigate if whole-body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only.

METHODS

A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole-body positron emission tomography-CT scans at our institution were collected. We used our in-house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field-of-views of the chest CT scans and the whole-body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole-body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten-fold cross-validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R-squared (R² ) were used as the performance metrics to assess the model performance.

RESULTS

The R² values when estimating volumes of whole-body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively.

CONCLUSION

Our algorithm shows promise in its ability to estimate whole-body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar.

Sarcopenia as a Predictor of Short- and Long-Term Outcomes in Patients Surgically Treated for Malignant Pleural Mesothelioma

Simple Summary

Malignant pleural mesothelioma (MPM) is an aggressive asbestos-related tumor with a poor prognosis. Surgery, often considered in the context of multimodality treatment, may be burdened by high morbidity, and for this reason, it should be reserved for patients who have a good pre-operative performance status. Sarcopenia, a well-established predictor of negative outcomes in several clinical settings, is still underinvestigated in MPM. The aim of the study is to elucidate the prognostic impact of muscular loss on surgical outcomes in patients with MPM. We demonstrated that, respectively, pre- and post-operative sarcopenia strongly affects the risk of post-operative complications and long-term survival after surgery for MPM. This finding will help clinicians to perform a better selection of patients, taking into consideration the enrollment in dedicated rehabilitation programs before surgery.

Abstract

Surgery for malignant pleural mesothelioma (MPM) should be reserved only for patients who have a good performance status. Sarcopenia, a well-known predictor of poor outcomes after surgery, is still underinvestigated in MPM. The aim of this study is to evaluate the role of sarcopenia as a predictor of short-and long-term outcomes in patients surgically treated for MPM. In our analysis, we included patients treated with a cytoreductive intent in a multimodality setting, with both pre- and post-operative CT scans without contrast available. We excluded those in whom a complete macroscopic resection was not achieved. Overall, 86 patients were enrolled. Sarcopenia was assessed by measuring the mean muscular density of the bilateral paravertebral muscles (T12 level) on pre-and post-operative CTs; a threshold value of 30 Hounsfield Units (HU) was identified. Sarcopenia was found pre-operatively in 57 (66%) patients and post-operatively in 61 (74%). Post-operative sarcopenic patients had a lower 3-year overall survival (OS) than those who were non-sarcopenic (34.9% vs. 57.6% p = 0.03). Pre-operative sarcopenia was significantly associated with a higher frequency of post-operative complications (65% vs. 41%, p = 0.04). The evaluation of sarcopenia, through a non-invasive method, would help to better select patients submitted to surgery for MPM in a multimodality setting.

The association between skeletal muscle measures and chemotherapy-induced toxicity in non-small cell lung cancer patients

BACKGROUND

Chemotherapy-induced toxicities frequently occur in non-small cell lung cancer (NSCLC) patients treated with platinum-based chemotherapy. Low skeletal muscle mass (SMM) has been associated with a higher incidence of toxicities for several types of cancers and cytostatics. The aim of this study was to evaluate the association between skeletal muscle measures and chemotherapy-induced toxicity in a large cohort of NSCLC patients.

METHODS

A multicentre prospective follow-up study (PGxLUNG, NTR number NL5373610015) in NSCLC patients was conducted. Included were patients diagnosed with NSCLC (stage II-IV) treated with first-line platinum-based (cisplatin or carboplatin) chemotherapy of whom pretreatment imaging was available. Skeletal muscle area (SMA) segmentation was performed on abdominal imaging at the level of the third lumbar vertebra (L3). SMA at the level of L3 was corrected for squared height (m2 ) to yield the lumbar skeletal muscle mass index (LSMI). Skeletal muscle density (SMD) was calculated as the mean Hounsfield Unit (HU) of the segmented SMA. SMM and SMD were categorized as low, intermediate, and high, based on LSMI and mean HU tertiles, respectively. Chemotherapy-induced toxicity was scored using CTCAE v4.03 and categorized into haematological (anaemia, leukocytopenia, neutropenia, and thrombocytopenia), non-haematological (nephrotoxicity, neurotoxicity, and esophagitis), and dose-limiting toxicity (DLT) (treatment switch, delay, de-escalation, discontinuation, or hospitalization). The relationship between SMM, SMD, and toxicities was assessed with logistic regression modelling taking into account potential confounders like gender and body mass index (BMI).

RESULTS

In total, 297 patients (male n = 167, median age 64 years) were included. Haematological toxicity grade 3/4 was experienced in 36.6% (n = 108) of the patients, 24.6% (n = 73) experienced any non-haematological toxicity grade ≥2, and 55.6% (n = 165) any DLT. Multivariate logistic regression analysis showed that low SMM (ORadj 2.41, 95% CI 1.31-4.45, P = 0.005) and age at diagnosis >65 years (ORadj 1.76, 95% CI 1.07-2.90, P = 0.025) were statistically significantly associated with overall haematological toxicity grade 3/4. No statistically significant associations were found between low SMM or low SMD and non-haematological toxicities. Low SMM (ORadj 2.23, 95% CI 1.23-4.04, P = 0.008) and high SMD (ORadj 0.41, 95% CI 0.23-0.74, P = 0.003) were statistically significantly associated with a higher respectively lower risk of DLT.

CONCLUSIONS

Non-small cell lung cancer patients with pretreatment low SMM are at significant higher risk for haematological toxicities grade 3/4 and DLT. NSCLC patients with high SMD are at significant lower risk for DLT. Further studies should be aimed to investigate whether platinum dosing based on skeletal muscle measurements and/or improvement of pretreatment SMM/SMD could reduce the risk of toxicity without compromising efficacy.

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

The diagnosis of sarcopenia requires accurate muscle quantification. As an alternative to manual muscle mass measurement through computed tomography (CT), artificial intelligence can be leveraged for the automation of these measurements. Although generally difficult to identify with the naked eye, the radiomic features in CT images are informative. In this study, the radiomic features were extracted from L3 CT images of the entire muscle area and partial areas of the erector spinae collected from non-small cell lung carcinoma (NSCLC) patients. The first-order statistics and gray-level co-occurrence, gray-level size zone, gray-level run length, neighboring gray-tone difference, and gray-level dependence matrices were the radiomic features analyzed. The identification performances of the following machine learning models were evaluated: logistic regression, support vector machine (SVM), random forest, and extreme gradient boosting (XGB). Sex, coarseness, skewness, and cluster prominence were selected as the relevant features effectively identifying sarcopenia. The XGB model demonstrated the best performance for the entire muscle, whereas the SVM was the worst-performing model. Overall, the models demonstrated improved performance for the entire muscle compared to the erector spinae. Although further validation is required, the radiomic features presented here could become reliable indicators for quantifying the phenomena observed in the muscles of NSCLC patients, thus facilitating the diagnosis of sarcopenia.