Enhancing Diagnostic Precision: Evaluation of Preprocessing Filters in Simple Diffusion Kurtosis Imaging for Head and Neck Tumors.

Background: Our initial clinical study using simple diffusion kurtosis imaging (SDI), which simultaneously produces a diffusion kurtosis image (DKI) and an apparent diffusion coefficient map, confirmed the usefulness of SDI for tumor diagnosis. However, the obtained DKI had noticeable variability in the mean kurtosis (MK) values, which is inherent to SDI. We aimed to improve this variability in SDI by preprocessing with three different filters (Gaussian [G], median [M], and nonlocal mean) of the diffusion-weighted images used for SDI. Methods: The usefulness of filter parameters for diagnosis was examined in basic and clinical studies involving 13 patients with head and neck tumors. Results: The filter parameters, which did not change the median MK value, but reduced the variability and significantly homogenized the MK values in tumor and normal tissues in both basic and clinical studies, were identified. In the receiver operating characteristic curve analysis for distinguishing tumors from normal tissues using MK values, the area under curve values significantly improved from 0.627 without filters to 0.641 with G (σ = 0.5) and 0.638 with M (radius = 0.5). Conclusions: Thus, image pretreatment with G and M for SDI was shown to be useful for improving tumor diagnosis in clinical practice.

Characteristic Mean Kurtosis Values in Simple Diffusion Kurtosis Imaging of Dentigerous Cysts.

We evaluated the usefulness of simple diffusion kurtosis (SD) imaging, which was developed to generate diffusion kurtosis images simultaneously with an apparent diffusion coefficient (ADC) map for 27 cystic disease lesions in the head and neck region. The mean kurtosis (MK) and ADC values were calculated for the cystic space. The MK values were dentigerous cyst (DC): 0.74, odontogenic keratocyst (OKC): 0.86, ranula (R): 0.13, and mucous cyst (M): 0, and the ADC values were DC: 1364 × 10-6 mm2/s, OKC: 925 × 10-6 mm2/s, R: 2718 × 10-6 mm2/s, and M: 2686 × 10-6 mm2/s. The MK values of DC and OKC were significantly higher than those of R and M, whereas their ADC values were significantly lower. One reason for the characteristic signal values in diffusion-weighted images of DC may be related to content components such as fibrous tissue and exudate cells. When imaging cystic disease in the head and neck region using SD imaging, the maximum b-value setting at the time of imaging should be limited to approximately 1200 s/mm2 for accurate MK value calculation. This study is the first to show that the MK values of DC are characteristically higher than those of other cysts.

Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath­hold radiotherapy after breast cancer surgery.

Increased heart dose during postoperative radiotherapy (RT) for left-sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath-hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left-sided BC. However, treatment planning and DIBH for RT are laborious, time-consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre-select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right-left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve-receiver operating characteristic of 0.88, for a cut-off value of 300 cGy. The present study suggested that ML can be used to pre-select female Asian patients with low MHD who do not require DIBH for the postoperative RT of BC.

Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients.

Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient's body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen's kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD ≥ 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH.

Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging.

Magnetic resonance (MR) images require a process known as windowing for optimizing the display conditions. However, the conventional windowing process often fails to achieve the preferred display conditions for observers due to various factors. This study proposes a novel framework for predicting the preferred windowing parameters for each observer using Bayesian statistical modeling. MR images obtained from 1000 patients were divided into training and test sets at a 7:3 ratio. The image intensity and windowing parameters were standardized using previously reported methods. Bayesian statistical modeling was utilized to predict the windowing parameters preferred by three MR imaging (MRI) operators. The performance of the proposed framework was evaluated by assessing the mean relative error (MRE), mean absolute error (MAE), and Pearson's correlation coefficient (ρ) of the test set. In addition, the naive method, which presumes that the average value of the windowing parameters for each acquisition sequence and body region in the training set is optimal, was also used for comparison. Three MRI operators and three radiologists conducted visual assessments. The mean MRE, MAE, and ρ values for the window level and width (WL/WW) in the proposed framework were 12.6 and 13.9, 42.9 and 85.4, and 0.98 and 0.98, respectively. These results outperformed those obtained using the naive method. The visual assessments revealed no significant differences between the original and predicted display conditions, indicating that the proposed framework accurately predicts individualized windowing parameters with the additional advantages of robustness and ease of use. Thus, the proposed framework can effectively predict the windowing parameters preferred by each observer.

Estimating the differences between inter-operator contrast enhancement in cerebral CT angiography.

BACKGROUND: Computed tomography (CT) angiography (CTA) is a non-invasive imaging method used to detect arteries and examine various brain diseases. When CTA is performed for follow-up or postoperative evaluation, reproducibility of vessel delineation is required. A reproducible and stable contrast enhancement can be achieved by manipulating the factors affecting it. Previous studies have investigated several factors that alter the contrast enhancement of arteries. However, no reports establishing the effect of different operators on contrast enhancement exist. PURPOSE: To assess the differences between inter-operator arterial contrast enhancement in cerebral CTA using Bayesian statistical modeling. METHODS: Image data were obtained using a multistage sampling method from the cerebral CTA scans of patients who underwent the process between January 2015 and December 2018. Several Bayesian statistical models were developed, and the objective variable was the mean CT number of the bilateral internal carotid arteries after contrast enhancement. The explanatory variables were sex, age, fractional dose (FD), and the operator's information. The posterior distributions of the parameters were computed via Bayesian inference using the Markov chain Monte Carlo (MCMC) method, with the Hamiltonian Monte Carlo method employed as the algorithm. The posterior predictive distributions were computed using the posterior distributions of the parameters. Finally, the differences between inter-operator arterial contrast enhancement on the CT number in cerebral CTA were estimated. RESULTS: The posterior distributions showed that all parameters representing the difference between operators included zero at the 95% credible intervals (CIs). The maximum mean difference between inter-operator CT number in the posterior predictive distribution was only 12.59 Hounsfield units (HUs). CONCLUSIONS: The Bayesian statistical modeling results suggest that contrast enhancement of cerebral CTA examination between operator-to-operator differences in postcontrast CT number was small compared to those within-operator differences resulting from factors not considered in the model.

Usefulness of Simple Diffusion Kurtosis Imaging for Head and Neck Tumors: An Early Clinical Study.

Diffusion kurtosis (DK) imaging (DKI), a type of restricted diffusion-weighted imaging, has been reported to be useful for tumor diagnoses in clinical studies. We developed a software program to simultaneously create DK images with apparent diffusion coefficient (ADC) maps and conducted an initial clinical study. Multi-shot echo-planar diffusion-weighted images were obtained at b-values of 0, 400, and 800 sec/mm2 for simple DKI, and DK images were created simultaneously with the ADC map. The usefulness of the DK image and ADC map was evaluated using a pixel analysis of all pixels and a median analysis of the pixels of each case. Tumor and normal tissues differed significantly in both pixel and median analyses. In the pixel analysis, the area under the curve was 0.64 for the mean kurtosis (MK) value and 0.77 for the ADC value. In the median analysis, the MK value was 0.74, and the ADC value was 0.75. The MK and ADC values correlated moderately in the pixel analysis and strongly in the median analysis. Our simple DKI system created DK images simultaneously with ADC maps, and the obtained MK and ADC values were useful for differentiating head and neck tumors from normal tissue.

Dosimetric comparison of pencil beam scanning proton therapy with or without multi-leaf collimator versus volumetric-modulated arc therapy for treatment of malignant glioma.

This study aimed to examine the dosimetric effect of intensity-modulated proton therapy (IMPT) with a multi-leaf collimator (MLC) in treating malignant glioma. We compared the dose distribution of IMPT with or without MLC (IMPTMLC+ or IMPTMLC-, respectively) using pencil beam scanning and volumetric-modulated arc therapy (VMAT) in simultaneous integrated boost (SIB) plans for 16 patients with malignant gliomas. High- and low-risk target volumes were assessed using D2%, V90%, V95%, homogeneity index (HI), and conformity index (CI). Organs at risk (OARs) were evaluated using the average dose (Dmean) and D2%. Furthermore, the dose to the normal brain was evaluated using from V5Gy to V40Gy at 5 Gy intervals. There were no significant differences among all techniques regarding V90%, V95%, and CI for the targets. HI and D2% for IMPTMLC+ and IMPTMLC- were significantly superior to those for VMAT (p < 0.01). The Dmean and D2% of all OARs for IMPTMLC+ were equivalent or superior to those of other techniques. Regarding the normal brain, there was no significant difference in V40Gy among all techniques whereas V5Gy to V35Gy in IMPTMLC+ were significantly smaller than those in IMPTMLC- (with differences ranging from 0.45% to 4.80%, p < 0.05) and VMAT (with differences ranging from 6.85% to 57.94%, p < 0.01). IMPTMLC+ could reduce the dose to OARs, while maintaining target coverage compared to IMPTMLC- and VMAT in treating malignant glioma.

Evaluation of calculation processes of apparent diffusion coefficient subtraction method (ASM) imaging.

A number of restricted diffusion (RD) imaging techniques, such as diffusion kurtosis (DK) imaging and Q space imaging, have been developed and proven to be useful for the diagnosis of diseases, including cerebral gliomas and cerebrovascular infarction. In particular, apparent diffusion coefficient (ADC) subtraction method (ASM) imaging has become available recently as a novel RD imaging technique. ASM is based on the difference between the ADC values in an image pair of two ADC maps, ADC basic (ADCb) and ADC modify (ADCm), which are created from diffusion-weighted images taken using short and long effective diffusion times, respectively. The present study aimed to assess the potential of different types of ASM imaging by comparing them with DK imaging which is the gold-standard RD imaging technique. In the present basic study using both polyethylene glycol phantom and cell-containing bio-phantom, three different types of ASM images were created using different calculation processes. ASM/A is an image calculated by dividing the absolute difference between ADCb and ADCm by ADCb several times. By contrast, ASM/S is an image created by dividing the absolute difference between ADCb and ADCm by the standard deviation of ADCb several times. As for positive ASM/A image (PASM/A), the positive image, which was resultant after subtracting ADCb from ADCm, was divided by ADCb several times. A comparison was made between the types of ASM and DK images. The results showed the same tendency between ASM/A in addition to both ASM/S and PASM/A. By increasing the number of divisions by ADCb from three to five times, ASM/A images transformed from DK-mimicking to more RD-sensitive images compared with DK images. These observations suggest that ASM/A images may prove useful for future clinical applications in RD imaging protocols for the diagnosis of diseases.

Quantitative evaluation of the reduction of distortion and metallic artifacts in magnetic resonance images using the multiacquisition variable­resonance image combination selective sequence.

Magnetic resonance imaging (MRI) is superior to computed tomography (CT) in determining changes in tissue structure, such as those observed following inflammation and infection. However, when metal implants or other metal objects are present, MRI exhibits more distortion and artifacts compared with CT, which hinders the accurate measurement of the implants. A limited number of reports have examined whether the novel MRI sequence, multiacquisition variable-resonance image combination selective (MAVRIC SL), can accurately measure metal implants without distortion. Therefore, the present study aimed to demonstrate whether MAVRIC SL could accurately measure metal implants without distortion and whether the area around the metal implants could be well delineated without artifacts. An agar phantom containing a titanium alloy lumbar implant was used for the present study and was imaged using a 3.0 T MRI machine. A total of three imaging sequences, namely MAVRIC SL, CUBE and magnetic image compilation (MAGiC), were applied and the results were compared. Distortion was evaluated by measuring the screw diameter and distance between the screws multiple times in the phase and frequency directions by two different investigators. The artifact region around the implant was examined using a quantitative method following standardization of the phantom signal values. It was revealed that MAVRIC SL was a superior sequence compared with CUBE and MAGiC, as there was significantly less distortion, a lack of bias between the two different investigators and significantly reduced artifact regions. These results suggested the possibility of utilizing MAVRIC SL for follow-up to observe metal implant insertions.

Chemical Constituents of Callistemon subulatus and Their Anti-Pancreatic Cancer Activity against Human PANC-1 Cell Line.

An n-hexane extract of Callistemon subulatus was found to exhibit potent cytotoxicity against PANC-1 human pancreatic cancer cells, preferentially under nutrition starvation conditions, with a PC50 value of 6.2 µg/mL. Phytochemical investigation of this bioactive extract resulted in the isolation of fifteen compounds (1-15), including a new compound, subulatone A (-). The structure of compound 1 was elucidated using HRFABMS and NMR spectroscopic analyses. The isolated compounds were tested for their preferential cytotoxicity against the PANC-1 human pancreatic cancer cell line, using an anti-austerity strategy. Among these, myrtucommulone A (2) showed highly potent preferential cytotoxicity, with a PC50 value of 0.28 µM. Myrtucommulone A (2) was found to alter PANC-1 cell morphology, inhibit cell migration, and downregulate the PI3K/Akt/mTOR and autophagy signaling pathways in nutrient-deprived media, leading to cancer cell death. Therefore, myrtucommulone A (2) is a lead compound for anticancer drug development based on an anti-austerity strategy.

Evaluation of Fast Diffusion Kurtosis Imaging Using New Software Designed for Widespread Clinical Use.

Clinical research using restricted diffusion-weighted imaging, especially diffusion kurtosis (DK) imaging, has been progressing, with reports on its effectiveness in the diagnostic imaging of cerebral infarctions, neurodegenerative diseases, and tumors, among others. However, the application of DK imaging in daily clinical practice has not spread because of the long imaging time required and the use of specific software for image creation. Herein, with the aim of promoting clinical research using DK imaging at any medical facility, we evaluated fast DK imaging using a new software program. We developed a new macro program that produces DK images using general-purpose, inexpensive software (Microsoft Excel and ImageJ), and we evaluated fast DK imaging using bio-phantoms and a healthy volunteer in clinical trials. The DK images created by the new software with diffusion-weighted images captured with short-time imaging sequences were similar to the original DK images captured with long-time imaging sequences. The DK images using three b-values, which can reduce the imaging time by 43%, were equivalent to the DK images using five b-values. The DK imaging technique developed herein might allow any medical facility to increase its daily clinical use of DK imaging and easily conduct clinical research.

Development and Evaluation of a Short-time Imaging Method for the Clinical Study of the Apparent Diffusion Coefficient Subtraction Method.

The apparent diffusion coefficient subtraction method (ASM) was developed as a new restricted diffusionweighted imaging technique for magnetic resonance imaging (MRI). The usefulness of the ASM has been established by in vitro basic research using a bio-phantom, and clinical research on the application of the ASM for the human body is needed. Herein, we developed a short-time sequence for ASM imaging of the heads of healthy volunteers (n=2), and we investigated the similarity between the obtained ASM images and diffusion kurtosis (DK) images to determine the utility of the ASM for clinical uses. This study appears to be the first to report ASM images of the human head. We observed that the short-time sequence for the ASM imaging of the head can be scanned in approx. 3 min at 1.5T MRI. The noise reduction effect of median filter processing was confirmed on the ASM images scanned by this sequence. The obtained ASM images showed a weak correlation with the DK images, indicating that the ASM images are restricted diffusion-weighted images. The new shorttime imaging sequence could thus be used in clinical studies applying the ASM.

New field-in-field with two reference points method for whole breast radiotherapy: Dosimetric analysis and radiation-induced skin toxicities assessment.

The usefulness of the field-in-field with two reference points (FIF w/ 2RP) method, in which the dose reference points are set simultaneously at two positions in the irradiation field and the high-dose range is completely eliminated, was examined in the present study with the aim of decreasing acute skin toxicity in adjuvant breast radiotherapy (RT). A total of 573 patients with breast cancer who underwent postoperative whole breast RT were classified into 178 cases with wedge (W) method, 142 cases with field-in-field without 2 reference points (FIF w/o 2RP) method and 253 cases with FIF w/ 2RP method. Using the FIF w/ 2RP method, the high-dose range was the lowest among the three irradiation methods. The planning target volume (PTV) V105% and the breast PTV for evaluation (BPe) V105% decreased to 0.09 and 0.10%, respectively. The FIF w/ 2RP method vs. the FIF w/o 2RP method had a strong association (η) with PTV V105% (η=0.79; P<0.001) and BPe V105% (η=0.76; P<0.001). The FIF w/ 2RP method had a significant impact on lowering the skin toxicity grade in weeks 3 and 4, and increasing the occurrence of skin toxicity grade 0. The FIF w/ 2RP method vs. the W method had a moderate association with skin toxicity grade at week 3 (η=0.49; P<0.001). Using the FIF w/ 2RP method, the high-dose range V105% of the target decreased to 0%, and skin adverse events were decreased in conjunction. For patients with early-stage breast cancer, particularly patients with relatively small-sized breasts, the FIF w/ 2RP method may be an optimal irradiation method.

Clinical Outcome of Palliative Concurrent Chemoradiotherapy with Cisplatin/Docetaxel for Stage III Non-small Cell Lung Cancer.

Palliative concurrent chemoradiotherapy (CCRT) is often administered to patients with stage III non-small cell lung cancer (NSCLC). We investigated the clinical outcomes of patients receiving palliative CCRT for NSCLC. Data of patients with NSCLC who underwent palliative CCRT (n=16), preoperative CCRT plus surgery (n=97), or definitive CCRT (n=48) were evaluated. In all groups, the concurrent chemotherapy regimens consisted of cisplatin and docetaxel. Rates of local control (LC), distant metastasis-free survival (DMFS), progression-free survival (PFS), overall survival (OS), and prognosis were compared. The 2-year rates of LC, DMFS, PFS, and OS in 16 patients who underwent palliative CCRT were 44.4%, 12.5%, 12.5%, and 18.8%, respectively. Univariate analysis showed that palliative CCRT was associated with poor LC (p<0.001), DMFS (p<0.001), PFS (p<0.001), and OS (p<0.001) outcomes in patients who completed CCRT as a preoperative treatment and poor LC (p=0.01), DMFS (p=0.003), PFS (p=0.04), and OS (p=0.004) outcomes in patients who were considered for definitive CCRT. Although there were some long-term survivors, the clinical outcomes of palliative CCRT were significantly inferior to those of the ideal treatments. Therefore, careful determination of the appropriate treatment indications and further studies are warranted.

Investigation into the Effect of Breast Volume on Irradiation Dose Distribution in Asian Women with Breast Cancer.

Reports on irradiation dose distribution in breast cancer radiotherapy with sufficient sample size are limited in Asian patients. Elucidating dose distribution in Asian patients is particularly important as their breast volume differs compared to patients in Europe and North America. Here, we examined dose distribution in the irradiation field relative to breast volume for three irradiation methods historically used in our facility. We investigated the influence of breast volume on each irradiation method for Asian women. A total of 573 women with early-stage breast cancer were treated with breast-conserving surgery and adjuvant radiotherapy. Three methods were compared: wedge (W), field-in-field (FIF), and wedge-field-in-field (W-FIF). In patients with small breast volume, FIF decreased low- and high-dose areas within the planning target volume, and increased optimal dose area more than W. In patients with medium and large breast volumes, FIF decreased high-dose area more than W. The absolute values of correlation coefficients of breast volume to low-, optimal-, and high-dose areas and mean dose were significantly lower in FIF than in W. The correlation coefficients of V107% were 0.00 and 0.28 for FIF and W, respectively. FIF is an excellent irradiation method that is less affected by breast volume than W in Asian breast cancer patients.

Sarcopenia is associated with poor prognosis after chemoradiotherapy in patients with stage III non-small-cell lung cancer: a retrospective analysis.

We intended to investigate whether muscle and adipose masses were associated with prognosis among patients with stage III non-small-cell lung cancer (NSCLC) who were undergoing chemoradiotherapy (CCRT). We retrospectively explored data of patients with stage III NSCLC who underwent definitive CCRT (≥ 60 Gy) between January 2004 and March 2018 at our hospital. We examined the relationship of overall survival (OS) with body mass index (BMI), skeletal muscle index (SMI), psoas muscle index (PMI), visceral adipose tissue index (VAI), subcutaneous adipose tissue index (SAI), and visceral-to-subcutaneous adipose tissue area ratio (VSR) using log-rank tests for the univariate analysis and Cox proportional hazard models for the multivariate analysis. Overall, 16, 32, and 12 patients had stage IIIA, IIIB, and IIIC NSCLC, respectively. The total radiotherapy dose ranged from 60 Gy/30 fractions to 66 Gy/33 fractions. In the univariate analysis, the performance status (PS), BMI, and SMI were associated with OS, whereas the PMI, VAI, SAI, and VSR were not. In the multivariate analysis, the PS and SMI were associated with OS. The hazard ratios and 95% confidence intervals were 2.91 and 1.28-6.64 for PS, and 2.36 and 1.15-4.85 for SMI, respectively. The 1, 3, and 5-year OS rates were 92.1%, 59.6%, and 51.0% in patients with high SMI, and 63.6%, 53.8%, and 17.9% in patients with low SMI, respectively. The SMI correlated with prognosis in our study population, whereas adipose mass did not. Therefore, sarcopenia should be considered while predicting the OS in such patients.

Evaluation of the Imaging Process for a Novel Subtraction Method Using Apparent Diffusion Coefficient Values.

Diffusion-weighted imaging may be used to obtain the apparent diffusion coefficient (ADC), which aids the diagnosis of cerebral infarction and tumors. An ADC reflects elements of free diffusion. Diffusion kurtosis imaging (DKI) has attracted attention as a restricted diffusion imaging technique. The ADC subtraction method (ASM) was developed to visualize restricted diffusion with high resolution by using two ADC maps taken with different diffusion times. We conducted the present study to provide a bridge between the reported basic ASM research and clinical research. We developed new imaging software for clinical use and evaluated its performance herein. This software performs the imaging process automatically and continuously at the pixel level, using ImageJ software. The new software uses a macro or a plugin which is compatible with various operating systems via a Java Virtual Machine. We tested the new imaging software's performance by using a Jurkat cell bio-phantom, and the statistical evaluation of the performance clarified that the ASM values of 99.98% of the pixels in the bio-phantom and physiological saline were calculated accurately (p<0.001). The new software may serve as a useful tool for future clinical applications and restricted diffusion imaging research.

Sarcopenia is related to poor prognosis in patients after trimodality therapy for locally advanced non-small cell lung cancer.

BACKGROUND: The association between sarcopenia and prognosis in patients with locally advanced non-small cell lung cancer (NSCLC) undergoing trimodality therapy, consisting of preoperative concurrent chemoradiotherapy and surgery, has not been reported. Therefore, we aimed to investigate the association of sarcopenia and fat mass with prognosis after trimodality therapy. METHODS: To assess sarcopenia, the psoas muscle mass was measured. Using computed tomography data, including third lumbar vertebra level images, psoas muscle mass and visceral and subcutaneous fat mass were measured. Additionally, body mass indices, and visceral/subcutaneous fat ratio, obtained by dividing the visceral fat index by the subcutaneous fat index, were calculated. We investigated the relationship between these parameters and overall survival. RESULTS: Ninety-nine eligible patients were included. In the univariate analysis, age, clinical stage, tumor location, psoas muscle index, and visceral/subcutaneous fat ratio were significant prognostic factors for overall survival (P = 0.008, P = 0.04, P = 0.04, P = 0.02, and P = 0.02, respectively). In the multivariate analysis, age and psoas muscle index were significant prognostic factors for overall survival (P = 0.01 and P = 0.03, respectively). The 5-year overall survival rates for the high and low psoas muscle index groups were 79.6% [95% confidence interval (CI), 67.1-94.5%] and 66.2% (95% CI, 54.1-81.1%), respectively; whereas, the 10-year overall survival rates were 61.9% (95% CI, 42.0-91.4%) and 25.3% (95% CI, 8.6-74.2%), respectively. CONCLUSION: Sarcopenia was related to poor overall survival in patients with locally advanced NSCLC undergoing trimodality therapy. Assessment of body composition prior to treatment may provide important information for formulating rational therapeutic strategies.