Liquid biopsy using cfDNA to predict radiation therapy response in solid tumors.

PURPOSE: This study explored the potential feasibility of cell-free DNA (cfDNA) in monitoring treatment response through the measurement of chromosomal instabilities using I-scores in the context of radiation therapy (RT) for other solid tumors. MATERIALS AND METHODS: This study enrolled 23 patients treated with RT for lung, esophageal, and head and neck cancer. Serial cfDNA monitoring was performed before RT, 1 week after RT, and 1 month after RT. Low-depth whole-genome sequencing was done using Nano kit and NextSeq 500 (Illumina Inc.). To measure the extent of genome-wide copy number instability, I-score was calculated. RESULTS: Pretreatment I-score was elevated to more than 5.09 in 17 patients (73.9%). There was a significant positive correlation between the gross tumor volume and the baseline I-score (Spearman rho = 0.419, p = 0.047). The median I-scores at baseline, post-RT 1 week (P1W), and post-RT 1 month (P1M) were 5.27, 5.13, and 4.79, respectively. The I-score at P1M was significantly lower than that at baseline (p = 0.002), while the difference between baseline and P1W was not significant (p = 0.244). CONCLUSION: We have shown the feasibility of cfDNA I-score to detect minimal residual disease after RT in patients with lung cancer, esophageal cancer, and head and neck cancer. Additional studies are ongoing to optimize the measurement and analysis of I-scores to predict the radiation response in cancer patients.

Feasibility of an Interactive Health Coaching Mobile App to Prevent Malnutrition and Muscle Loss in Esophageal Cancer Patients Receiving Neoadjuvant Concurrent Chemoradiotherapy: Prospective Pilot Study.

BACKGROUND: Excessive muscle loss is an important prognostic factor in esophageal cancer patients undergoing neoadjuvant chemoradiotherapy (NACRT), as reported in our previous research. OBJECTIVE: In this pilot study, we prospectively tested the feasibility of a health coaching mobile app for preventing malnutrition and muscle loss in this patient population. METHODS: Between July 2019 and May 2020, we enrolled 38 male patients with esophageal cancer scheduled for NACRT. For 8 weeks from the start of radiotherapy (RT), the patients used Noom, a health coaching mobile app that interactively provided online advice about food intake, exercise, and weight changes. The skeletal muscle index (SMI) measured based on computed tomography and nutrition-related laboratory markers were assessed before and after RT. We evaluated the changes in the SMI, nutrition, and inflammatory factors between the patient group that used the mobile app (mHealth group) and our previous study cohort (usual care group). Additionally, we analyzed the factors associated with walk steps recorded in the app. RESULTS: Two patients dropped out of the study (no app usage; treatment changed to a definitive aim). The use (or activation) of the app was noted in approximately 70% (25/36) of the patients until the end of the trial. Compared to the 1:2 matched usual care group by propensity scores balanced with their age, primary tumor location, tumor stage, pre-RT BMI, and pre-RT SMI level, 30 operable patients showed less aggravation of the prognostic nutritional index (PNI) (-6.7 vs -9.8; P=.04). However, there was no significant difference in the SMI change or the number of patients with excessive muscle loss (∆SMI/50 days >10%). In patients with excessive muscle loss, the walk steps significantly decreased in the last 4 weeks compared to those in the first 4 weeks. Age affected the absolute number of walk steps (P=.01), whereas pre-RT sarcopenia was related to the recovery of the reduced walk steps (P=.03). CONCLUSIONS: For esophageal cancer patients receiving NACRT, a health care mobile app helped nutritional self-care with less decrease in the PNI, although it did not prevent excessive muscle loss. An individualized care model with proper exercise as well as nutritional support may be required to reduce muscle loss and malnutrition.

Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer.

BACKGROUND: Excessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour-intensive. In this study, we built machine-learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results. METHODS: We randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine-learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio over 50 days. According to our previous study results, which used the maximal χ2 method, 10.0% decrease of skeletal muscle index over 50 days was determined as the cut-off value to define the excessive skeletal muscle loss. RESULTS: The five input variables were significantly different between the excessive and the non-excessive muscle loss group (all P < 0.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve = 0.808, 95% confidence interval (CI): 0.708-0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%-89.5%) and 74.5% (95% CI: 62.7%-86.3%), respectively. CONCLUSIONS: Machine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT.

Early Clinical Outcomes of Intensity Modulated Radiation Therapy/Intensity Modulated Proton Therapy Combination in Comparison with Intensity Modulated Radiation Therapy Alone in Oropharynx Cancer Patients.

PURPOSE: To report the early clinical outcomes of combining intensity-modulated radiation therapy (IMRT) and intensity-modulated proton therapy (IMPT) in comparison with IMRT alone in treating oropharynx cancer (OPC) patients. MATERIALS AND METHODS: The medical records of 148 OPC patients who underwent definitive radiotherapy (RT) with concurrent systemic therapy, from January 2016 till December 2019 at Samsung Medical Center, were retrospectively reviewed. During the 5.5 weeks' RT course, the initial 16 (or 18) fractions were delivered by IMRT in all patients, and the subsequent 12 (or 10) fractions were either by IMRT in 81 patients (IMRT only) or by IMPT in 67 (IMRT/IMPT combination), respectively, based on comparison of adaptive re-plan profiles and availability of equipment. Propensity-score matching (PSM) was done on 76 patients (38 from each group) for comparative analyses. RESULTS: With the median follow-up of 24.7 months, there was no significant difference in overall survival and progression free survival between groups, both before and after PSM. Before PSM, the IMRT/IMPT combination group experienced grade ≥ 3 acute toxicities less frequently: mucositis in 37.0% and 13.4% (p < 0.001); and analgesic quantification algorithm (AQA) in 37.0% and 19.4% (p = 0.019), respectively. The same trends were observed after PSM: mucositis in 39.5% and 15.8% (p = 0.021); and AQA in 47.4% and 21.1% (p = 0.016), respectively. In multivariate logistic regression, grade ≥ 3 mucositis was significantly less frequent in the IMRT/IMPT combination group, both before and after PSM (p = 0.027 and 0.024, respectively). AQA score ≥ 3 was also less frequent in the IMRT/IMPT combination group, both before and after PSM (p = 0.085 and 0.018, respectively). CONCLUSIONS: In treating the OPC patients, with comparable early oncologic outcomes, more favorable acute toxicity profiles were achieved following IMRT/IMPT combination than IMRT alone.

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients.

This study aimed to investigate the performance of a deep learning-based survival-prediction model, which predicts the overall survival (OS) time of glioblastoma patients who have received surgery followed by concurrent chemoradiotherapy (CCRT). The medical records of glioblastoma patients who had received surgery and CCRT between January 2011 and December 2017 were retrospectively reviewed. Based on our inclusion criteria, 118 patients were selected and semi-randomly allocated to training and test datasets (3:1 ratio, respectively). A convolutional neural network-based deep learning model was trained with magnetic resonance imaging (MRI) data and clinical profiles to predict OS. The MRI was reconstructed by using four pulse sequences (22 slices) and nine images were selected based on the longest slice of glioblastoma by a physician for each pulse sequence. The clinical profiles consist of personal, genetic, and treatment factors. The concordance index (C-index) and integrated area under the curve (iAUC) of the time-dependent area-under-the-curve curves of each model were calculated to evaluate the performance of the survival-prediction models. The model that incorporated clinical and radiomic features showed a higher C-index (0.768 (95% confidence interval (CI): 0.759, 0.776)) and iAUC (0.790 (95% CI: 0.783, 0.797)) than the model using clinical features alone (C-index = 0.693 (95% CI: 0.685, 0.701); iAUC = 0.723 (95% CI: 0.716, 0.731)) and the model using radiomic features alone (C-index = 0.590 (95% CI: 0.579, 0.600); iAUC = 0.614 (95% CI: 0.607, 0.621)). These improvements to the C-indexes and iAUCs were validated using the 1000-times bootstrapping method; all were statistically significant (p < 0.001). This study suggests the synergistic benefits of using both clinical and radiomic parameters. Furthermore, it indicates the potential of multi-parametric deep learning models for the survival prediction of glioblastoma patients.

Prognostic Impact of Sarcopenia and Skeletal Muscle Loss During Neoadjuvant Chemoradiotherapy in Esophageal Cancer.

BACKGROUNDS: The relationship between sarcopenia, characterized by loss of muscle mass and strength, and survival outcomes of esophageal cancer is controversial. This study aimed to assess the effect of sarcopenia and skeletal muscle loss on overall survival (OS) and recurrence-free survival (RFS) of esophageal cancer patients. METHODS: We retrospectively collected the medical records of 248 male patients diagnosed with squamous cell esophageal cancer and who underwent neoadjuvant chemoradiotherapy (NACRT) followed by surgery. We measured the cross-sectional area of the skeletal muscle at the L3 vertebra level using computed tomography images and calculated the skeletal muscle index (SMI). Sarcopenia was defined as SMI <52.4 cm2/m2, and excessive muscle loss was defined as SMI change <-10.0%/50 days during NACRT. Moreover, laboratory test results, such as albumin, prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) before and after NACRT, were collected. RESULTS: In the univariable Cox analysis, pre- (p = 0.689) and post-radiotherapy (RT) sarcopenia (p = 0.669) were not associated with OS. However, excessive muscle loss had a significant association with OS in both the univariable and multivariable analyses (all p = 0.001). Excessive muscle loss was also related to RFS in both the univariable (p = 0.011) and multivariable (p = 0.022) Cox analysis. Patients with excessive muscle loss had significantly lower levels of post-RT albumin (p < 0.001) and PNI (p < 0.001), higher levels of post-RT NLR (p = 0.031) and PLR (p = 0.071), larger decrease in albumin (p < 0.001) and PNI (p < 0.001) after NACRT, and larger increase in NLR (p = 0.051) and PLR (p = 0.088) after NACRT than in those with non-excessive muscle loss. CONCLUSION: Excessive muscle loss rather than pre- and post-RT sarcopenia was a significant prognostic factor for OS and RFS, and it was also related to nutritional and inflammatory markers.

Higher thoracic radiation dose is beneficial in patients with extensive small cell lung cancer.

PURPOSE: The effectiveness of thoracic radiation therapy (TRT) in extensive-stage small cell lung cancer (ES-SCLC) patients is increasingly reported, but there is no definite consensus on its application. The aim of this study was to identify factors associated with better outcomes of TRT among patients with ES-SCLC, focusing on whether a higher TRT dose could improve treatment outcome. MATERIALS AND METHODS: The medical records of 85 patients with ES-SCLC who received TRT between January 2008 and June 2017 were retrospectively reviewed. Eligibility criteria were a biological effective dose with α/ß = 10 (BED) higher than 30 Gy10 and completion of planned radiotherapy. RESULTS: During a median follow-up of 5.3 months, 68 patients (80.0%) experienced disease progression. In univariate analysis, a BED >50 Gy10 was a significant prognostic factor for overall survival (OS; 40.8% vs. 12.5%, p = 0.006), progression-free survival (PFS; 15.9% vs. 9.6%, p = 0.004), and intrathoracic PFS (IT-PFS; 39.3% vs. 20.5%, p = 0.004) at 1 year. In multivariate analysis, a BED >50 Gy10 remained a significant prognostic factor for OS (hazard ratio [HR] = 0.502; 95% confidence interval [CI], 0.287-0.876; p = 0.015), PFS (HR = 0.453; 95% CI, 0.265-0.773; p = 0.004), and IT-PFS (HR = 0.331; 95% CI, 0.171-0.641; p = 0.001). Response to the last chemotherapy was also associated with better OS in both univariate and multivariate analysis. CONCLUSION: A TRT dose of BED >50 Gy10 may be beneficial for patients with ES-SCLC. Further studies are needed to select patients who will most benefit from high-dose TRT.

Correlation between macular ganglion cell-inner plexiform layer thickness and visual acuity after resolution of the macular edema secondary to central retinal vein occlusion.

AIM: To examine the thickness of the ganglion cell-inner plexiform layer (GCIPL) in eyes with resolved macular edema (ME) in non-ischemic central retinal vein occlusion (CRVO), applying spectral-domain optical coherence tomography (SD-OCT), and its relationship with visual acuity. METHODS: The retrospective observational case-control study included 30 eyes of non-ischemic CRVO patients with resolved ME (ME eyes) after treatment, and 30 eyes of non-ischemic CRVO patients without ME (non-ME eyes). The macular GCIPL thickness, peripapillary retinal nerve fiber layer (pRNFL) thickness and central macular thickness (CMT) were measured on a SD-OCT scan. Linear regression analyses were performed to determine the correlation between the thickness of each and the visual acuity (VA). RESULTS: No significant difference in average GCIPL thickness, mean pRNFL thickness and CMT were observed between ME group and non-ME group (P=0.296, 0.183, 0.846). But, minimum GCIPL thickness was reduced in ME eyes compared with non-ME eyes (P=0.022). Final VA significantly correlated with the minimum GCIPL thickness in ME eyes (r=-0.482, P=0.007), whereas no correlation was found with average GCIPL thickness, average pRNFL thickness and mean CMT. CONCLUSION: Minimum GCIPL thickness is reduced in ME eyes compared with non-ME eyes, and correlated with the VA in non-ischemic CRVO. These results propose that inner retinal damage occurring in patients with ME secondary to non-ischemic CRVO may lead to permanent visual defect after treatment.

Different patterns in the risk of newly developed fatty liver and lipid changes with tamoxifen versus aromatase inhibitors in postmenopausal women with early breast cancer: A propensity score-matched cohort study.

BACKGROUND: Management of metabolic complications of long-term adjuvant endocrine therapy in early breast cancer remained an unmet need. We aimed to compare the effects of tamoxifen (TMX) and aromatase inhibitors (AIs) on the risk of fatty liver in conjunction with longitudinal changes in the serum lipid parameters. METHODS: Among 1203 subjects who were taking adjuvant TMX or AI (anastrozole or letrozole) without fatty liver at baseline, those taking TMX or AI were 1:1 matched on the propensity score. The primary outcome was newly developed fatty liver detected on annual liver ultrasonography. RESULTS: Among 328 matched subjects (mean age 53.5 years, body mass index 22.9 kg/m2), 62 cases of fatty liver in the TMX group and 41 cases in the AI group were detected in a total of 987.4 person-years. The incidence rate of fatty liver was higher in the TMX group than in the AI group (128.7 versus 81.1 per 1000 person-years, P = 0.021), particularly within the first 2 years of therapy. TMX was associated with an increased 5-year risk of newly developed fatty liver (adjusted hazard ratio 1.61, P = 0.030) compared with AI independent of obesity and cholesterol level. Subjects who developed fatty liver had higher triglycerides (TGs) and lower high-density lipoprotein cholesterol (HDL-C) level at baseline than those without, which was sustained during follow-up despite the serum cholesterol-lowering effect of TMX. CONCLUSIONS: TMX independently increased the 5-year risk of newly developed fatty liver compared with AI in postmenopausal women with early breast cancer. Our findings suggest the need for considering the risk of fatty liver as a different adverse event profile between AI and TMX, particularly in patients with obesity, high TGs and low HDL-C.