Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells.

It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system.

Successful bronchial artery embolization using hydrogel coils for hemoptysis during extracorporeal membrane oxygenation.

A 58-year-old woman with bronchiectasis presented with massive hemoptysis and severe respiratory failure, which required long-term extracorporeal membrane oxygenation with continuous heparin infusion. Bronchial artery embolization using hydrogel coils, which provide a greater volume occlusion than bare platinum coils, was performed; hemoptysis stopped and she fully recovered. No recanalization was observed on follow-up computed tomography angiography 2 months postbronchial artery embolization, and there had been no recurrence of bleeding at the time of this report (at least 6 months). Although continuous anticoagulation during extracorporeal membrane oxygenation might hinder complete vessel occlusion by metallic coils or induce early recanalization (because the homeostatic mechanism of coils depends on the patient's coagulability), our experience showed that bronchial artery embolization using hydrogel coils was effective and safe. Additionally, this case presents a successful example of anticoagulation management for patients with hemoptysis on extracorporeal membrane oxygenation who undergo bronchial artery embolization using coils.

Semi-quantitative evaluation of chest computed tomography for coronavirus disease 2019 in a critical care unit: A case-control study.

The spread of abnormal opacity on chest computed tomography (CT) has been reported as a predictor of coronavirus disease 2019 (COVID-19) severity; however, the relationship between CT findings and prognosis in patients with severe COVID-19 remains unclear. The objective of this study was to evaluate the extent of abnormal opacity on chest CT and its association with prognosis in patients with COVID-19 in a critical care medical center, using a simple semi-quantitative method. This single-center case-control study included patients diagnosed with severe COVID-19 pneumonia who were admitted to a critical care center. The diagnosis of COVID-19 was based on positive results of a reverse transcription polymerase chain reaction test. All patients underwent non-contrast whole-body CT upon admission. Six representative axial chest CT images were selected for each patient to evaluate the extent of lung lesions. The percentage of the area involved in the representative CT images was visually assessed by 2 radiologists and scored on 4-point scale to obtain the bedside CT score, which was compared between patients who survived and those who died using the Mann-Whitney U test. A total of 63 patients were included in this study: 51 survived and 12 died after intensive treatment. The inter-rater reliability of bedside scores between the 2 radiologists was acceptable. The median bedside CT score of the survival group was 12.5 and that of the mortality group was 16.5; the difference between the 2 groups was statistically significant. The degree of opacity can be easily scored using representative CT images in patients with severe COVID-19 pneumonia, without sophisticated software. A greater extent of abnormal opacity is associated with poorer prognosis. Predicting the prognosis of patients with severe COVID-19 could facilitate prompt and appropriate treatment.

Intra-fractional patient setup error during fractionated intracranial stereotactic irradiation treatment of patients wearing medical masks: comparison with and without bite block during COVID-19 pandemic.

The immobilization of patients with a bite block (BB) carries the risk of interpersonal infection, particularly in the context of pandemics such as COVID-19. Here, we compared the intra-fractional patient setup error (intra-SE) with and without a BB during fractionated intracranial stereotactic irradiation (STI). Fifteen patients with brain metastases were immobilized using a BB without a medical mask, while 15 patients were immobilized without using a BB and with a medical mask. The intra-SEs in six directions (anterior-posterior (AP), superior-inferior (SI), left-right (LR), pitch, roll, and yaw) were calculated by using cone-beam computed tomography images acquired before and after the treatments. We analyzed a total of 53 and 67 treatment sessions for the with- and without-BB groups, respectively. A comparable absolute mean translational and rotational intra-SE was observed (P > 0.05) in the AP (0.19 vs 0.23 mm with- and without-BB, respectively), SI (0.30 vs 0.29 mm), LR (0.20 vs 0.29 mm), pitch (0.18 vs 0.27°), roll (0.23 vs 0.23°) and yaw (0.27 vs 22°) directions. The resultant planning target volume (PTV) margin to compensate for intra-SE was <1 mm. No statistically significant correlation was observed between the intra-SE and treatment times. A PTV margin of <1 mm was achieved even when patients were immobilized without a BB during STI dose delivery.

Prolonged overall treatment time negatively affects the outcomes of stereotactic body radiotherapy for early-stage non-small-cell lung cancer: A propensity score-weighted, single-center analysis.

Previous studies have reported conflicting results for the effect of overall treatment time with stereotactic body radiotherapy on tumor control in early-stage non-small-cell lung cancer. To examine this effect, we conducted a propensity score-weighted, retrospective, observational study at a single institution. We analyzed the data of 200 patients with early-stage non-small-cell lung cancer who underwent stereotactic body radiotherapy (48 Gy in 4 fractions) at our institution between January 2007 and October 2013. Patients were grouped into consecutive (overall treatment time = 4-5 days, n = 116) or non-consecutive treatment groups (overall treatment time = 6-10 days, n = 84). The outcomes of interest were local control and overall survival. The Cox regression model was used with propensity score and inverse probability of treatment weighting. The median overall treatment times in the consecutive and non-consecutive groups were 4 and 6 days, respectively. The 5-year local control and overall survival rates in the consecutive vs. the non-consecutive group were 86.3 vs. 77.2% and 55.5 vs. 51.8%, respectively. After propensity score weighting, consecutive stereotactic body radiotherapy was associated with positive local control (adjusted hazard ratio 0.30, 95% confidence interval 0.14-0.65; p = 0.002) and overall survival (adjusted hazard ratio 0.56, 95% confidence interval 0.34-0.91; p = 0.019) benefits. The prolonged overall treatment time of stereotactic body radiotherapy treatment negatively affected the outcomes of patients with early-stage non-small-cell lung cancer. To our knowledge, this is the first study to show that in patients with early-stage non-small-cell lung cancer treated with the same dose-fractionation regimen, consecutive stereotactic body radiotherapy has a more beneficial effect on tumor control than non-consecutive stereotactic body radiotherapy.

Stereotactic body radiation therapy planning for liver tumors using functional images from dual-energy computed tomography.

PURPOSE: This study aimed to generate a functional image of the liver using dual-energy computed tomography (DECT) and a functional-image-based stereotactic body radiation therapy plan to minimize the dose to the volume of the functional liver (Vfl). MATERIAL AND METHODS: A normalized iodine density (NID) map was generated for fifteen patients with liver tumors. The volume of liver with an NID < 0.46 was defined as Vfl, and the ratio between Vfl and the total volume of the liver (FLR) was calculated. The relationship between the FLR and Fibrosis-4 (FIB-4) was assessed. For patients with 15% < FLR < 85%, functional volumetric modulated-arc therapy plans (F-VMAT) were retrospectively generated to preserve Vfl, and compared to the clinical plans (C-VMAT). RESULTS: FLR showed a significantly strong correlation with FIB-4 (r = -0.71, p < 0.01). For ten generated F-VMAT plans, the dosimetric parameters of D99%, D50%, D1% and the conformity index were comparable to those of the C-VMAT (p > 0.05). For Vfl, F-VMAT plans achieved lower V5Gy (122.4 ± 31.7 vs 181.1 ± 57.3 cc), V10Gy (44.4 ± 22.2 vs 98.2 ± 33.3 cc), V15Gy (22.6 ± 20.3 vs 49.8 ± 33.7 cc), V20Gy (11.6 ± 14.1 vs 24.9 ± 25.1 cc), and Dmean (3.9 ± 2.3 vs 5.8 ± 3.0 Gy) values than the C-VMAT plans (p < 0.01). CONCLUSIONS: The functional image derived from DECT was successfully used, allowing for a reduction in the dose to the Vfl without compromising target coverage.

Death unrelated to cancer and death from aspiration pneumonia after definitive radiotherapy for head and neck cancer.

BACKGROUND AND PURPOSE: The incidence of hypopharyngeal and supraglottic cancer (HSC) is high in Japan. This study aimed to retrospectively identify risk factors for death unrelated to cancer and death from aspiration pneumonia after definitive radiotherapy (RT) for HSC. MATERIALS AND METHODS: Overall, 391 patients who began definitive RT for HSC between 2006 and 2014 were identified from the Osaka International Cancer Institute electronic database. Among 391 patients, 33 had a history of surgery for esophageal cancer (EC) and 19 received simultaneous RT for synchronous EC. The cause of death was divided into 3 main categories: "cancer under study," "other malignancy," and "unrelated to cancer." Cox proportional hazard model was used to estimate the hazard ratio (HR). RESULTS: The median follow-up for survivors was 8 (range 3.6-14.1) years. At the last follow-up, 202 patients died. Death from "cancer under study," "other malignancy," and "unrelated to cancer" occurred in 92 (45.5%), 55 (27.2%), and 55 (27.2%) patients, respectively. Twelve patients died from aspiration pneumonia. In multivariate analysis for death unrelated to cancer and death from aspiration pneumonia, history of surgery for EC (HR: 3.87, p < 0.001; HR: 6.84, p = 0.007, respectively) and simultaneous RT for synchronous EC (HR: 3.74, p = 0.006; HR: 16.37, p < 0.001, respectively) were significant risk factors. CONCLUSION: The laryngeal preservation approach by RT for HSC patients with a history of surgery for EC and simultaneous RT for synchronous EC should be used with caution.

Elective Nodal Irradiation for Non-small Cell Lung Cancer Complicated With Chronic Obstructive Pulmonary Disease Affects Immunotherapy Αfter Definitive Chemoradiotherapy.

BACKGROUND/AIM: The aim of this retrospective study was to detect the frequency, reasons, and significant factors for not receiving immunotherapy after chemoradiotherapy in non-small cell lung cancer (NSCLC) patients. PATIENTS AND METHODS: Thirty-four patients with NSCLC received definitive chemoradiotherapy. The endpoint of this study was receiving durvalumab within 45 days after chemoradiotherapy for NSCLC. RESULTS: Twenty-five of 34 (73%) patients received immunotherapy within 45 days after chemoradiotherapy. The reasons for not receiving immunotherapy were radiation pneumonitis (50%), radiation esophagitis (10%), and four other reasons (40%). Univariate analysis showed that significant factors for not receiving immunotherapy were elective nodal irradiation (ENI)+ and chronic obstructive pulmonary disease (COPD)+. The rate of immunotherapy was 100% (17/17 cases) in the COPD- and ENI- group, and 16% (1/6 cases) in the COPD+ and ENI+ group. CONCLUSION: ENI for NSCLC complicated with COPD decreased the rate of immunotherapy after definitive chemoradiotherapy.

Ro 90-7501 Is a Novel Radiosensitizer for Cervical Cancer Cells that Inhibits ATM Phosphorylation.

BACKGROUND/AIM: Ro 90-7501 has been reported as an inhibitor of the amyloid ß42 fibril assembly that is associated with Alzheimer's disease. The present study aimed to elucidate the radiosensitizing effects of Ro 90-7501 and focused on ATM signaling after irradiation. MATERIALS AND METHODS: Clonogenic survival, apoptosis, and cell-cycle assays as well as western blotting were performed in HeLa cells treated with irradiation and Ro 90-7501. Tumor growth delay assay was also performed using BALB/c-nu mice. RESULTS: The combination of irradiation with Ro 90-7501 showed significant radiosensitizing effects in clonogenic survival and tumor growth delay assays. Ro 90-7501 significantly increased apoptosis and impaired cell cycle after irradiation. Western blotting showed that Ro 90-7501 suppressed the phosphorylation of ATM and its downstream proteins, such as H2AX, Chk1, and Chk2, after irradiation. CONCLUSION: Ro 90-7501 inhibits DNA damage response by inhibiting ATM and has significant radiosensitizing effects on cervical cancer cells.

The epitranscriptome m6A writer METTL3 promotes chemo- and radioresistance in pancreatic cancer cells.

N6-methyladenosine (m6A) is the most abundant epitranscriptome modification in mammalian mRNA. Recent years have seen substantial progress in m6A epitranscriptomics, indicating its crucial roles in the initiation and progression of cancer through regulation of RNA stabilities, mRNA splicing, microRNA processing and mRNA translation. However, by what means m6A is dynamically regulated or written by enzymatic components represented by methyltransferase-like 3 (METTL3) and how m6A is significant for each of the numerous genes remain unclear. We focused on METTL3 in pancreatic cancer, the prognosis of which is not satisfactory despite the development of multidisciplinary therapies. We established METTL3-knockdown pancreatic cancer cell line using short hairpin RNA. Although morphologic and proliferative changes were unaffected, METTL3-depleted cells showed higher sensitivity to anticancer reagents such as gemcitabine, 5-fluorouracil, cisplatin and irradiation. Our data suggest that METTL3 is a potent target for enhancing therapeutic efficacy in patients with pancreatic cancer. In addition, we performed cDNA expression analysis followed by gene ontology and protein-protein interaction analysis using the Database for Annotation, Visualization, and Integrated Discovery and Search Tool for the Retrieval of Interacting Genes/Proteins databases, respectively. The results demonstrate that METTL3 was associated with mitogen-activated protein kinase cascades, ubiquitin-dependent process and RNA splicing and regulation of cellular process, suggesting functional roles and targets of METTL3.

A Convolutional Neural Network Uses Microscopic Images to Differentiate between Mouse and Human Cell Lines and Their Radioresistant Clones.

: Artificial intelligence (AI) trained with a convolutional neural network (CNN) is a recent technological advancement. Previously, several attempts have been made to train AI using medical images for clinical applications. However, whether AI can distinguish microscopic images of mammalian cells has remained debatable. This study assesses the accuracy of image recognition techniques using the CNN to identify microscopic images. We also attempted to distinguish between mouse and human cells and their radioresistant clones. We used phase-contrast microscopic images of radioresistant clones from two cell lines, mouse squamous cell carcinoma NR-S1, and human cervical carcinoma ME-180. We obtained 10,000 images of each of the parental NR-S1 and ME-180 controls as well as radioresistant clones. We trained the CNN called VGG16 using these images and obtained an accuracy of 96%. Features extracted by the trained CNN were plotted using t-distributed stochastic neighbor embedding, and images of each cell line were well clustered. Overall, these findings suggest the utility of image recognition using AI for predicting minute differences among phase-contrast microscopic images of cancer cells and their radioresistant clones. SIGNIFICANCE: This study demonstrates rapid and accurate identification of radioresistant tumor cells in culture using artifical intelligence; this should have applications in future preclinical cancer research.