A new neural network model that detects graft ruptures and contralateral anterior cruciate ligament injuries.

PURPOSE: The purpose of this study was to develop a neural network model for predicting second anterior cruciate ligament (ACL) injury risk following ACL reconstruction using patient features from medical records. METHODS: Of 486 consecutive patients who underwent primary unilateral ACL reconstruction, 386 patients (198 women, 188 men) with a mean age of 25.1 ± 11.6 years were included in this study. Fifty-eight features, including demographic data, surgical, preoperative and postoperative data, were retrospectively collected from medical records, and features with an incidence of less than 5% were excluded. Finally, 14 features were used for the analysis. The multilayer perceptron was composed of four hidden layers with a rectified linear unit as activation and was trained to maximise the area under the receiver-operating characteristic curve (auROC). Subsequently, validation was carried out through a rigorous threefold cross-validation process. To ascertain the most efficacious combination of features with the highest auROC, a single feature with the least impact on auROC maximisation was systematically eliminated from the comprehensive variable set, ultimately resulting in the retention of a mere two variables. RESULTS: The median follow-up period was 50.5 (24-142) months. Fifty-seven knees had a second ACL injury, with a graft rupture rate of 7.7% and a contralateral injury rate of 6.9%. The maximum auROC for predicting graft rupture was 0.81 with two features: young age and hamstring graft. Meanwhile, the maximum auROC for predicting contralateral ACL injury was 0.74 with seven features, including young age, presence of medial meniscus tear, small body mass index, hamstring graft, female sex and medial meniscus repair or treatment. CONCLUSION: A neural network model with patient features from medical records detected graft ruptures and contralateral ACL injuries with acceptable accuracy. This model can serve as a new, useful tool in clinical practice to inform decisions about ACL reconstruction and retuning to sports postoperatively. LEVEL OF EVIDENCE: Level IV.

Stability of standardized uptake values for quantitative bone SPECT for jawbone lesions: a single-center cross-sectional study.

BACKGROUND: The long time required for bone uptake of radiopharmaceutical material after injection for bone scintigraphy is a burden for patients with poor health. Thus, to assess whether the uptake time could be reduced for single-photon emission computed tomography (SPECT) of the jawbone, this study evaluated differences in maximum standardized uptake values (SUVmax) within patients using SPECT imaging at 2 and 3 hours after radiopharmaceutical injection. METHODS: A total of 33 patients undergoing treatment or in post-treatment follow-up for medication-related osteonecrosis of the jaw, who visited our hospital between July 2020 and August 2021 and could receive SPECT twice on the same day, were enrolled in the study. Patients were injected with technetium-99 m hydroxymethylene diphosphonate (Tc-99 m HMDP) intravenously. The SUVmax for healthy parietal bones and jawbone lesions were calculated from the SPECT images using quantitative analysis software, and the SUVmax were compared between 2- and 3-hour uptake times. RESULTS: After exclusion, 30 patients were included in the study. In the 2-hour and 3-hour images, the median SUVmax of the parietal bones were 1.90 and 1.81, respectively, and those of the jawbone lesions were 9.25 and 9.39, respectively. The limits of agreement (LOA) ranged from - 0.33 to 0.25 in the parietal bones, and the %LOA ranged from - 9.8 to 17.3% in the jawbone lesions, showing high equivalence between the two uptake durations. The SUVmax showed no clinical differences between the 2- and 3-hour uptake durations for Tc-99 m HMDP SPECT of the jawbone. CONCLUSIONS: The results of this study justify a 2-3-hour uptake window when performing quantitative SPECT of the jawbone. Therefore, the minimum uptake time can potentially be reduced to only 2 hours.

Risk Factors Contributing to Disparities in Medical Treatment and Lower Survival Rates among Patients with Non-Small Cell Lung Cancer Induced by Residential Areas.

OBJECTIVE: Because of a lack of medical resources for cancer treatment, particularly in rural areas, there are disparities in receiving medical treatment between urban and rural area. This study examined the association between residential area or areal deprivation index (ADI) and lack of surgical treatment or chemotherapy in patients with non-small cell lung cancer (NSCLC) in rural area, Japan. METHODS: We analyzed the Aomori population-based cancer registry data from 926 cancer patients with NSCLC diagnosed between January and December 2014. Multivariable logistic regression and Cox proportional hazards models were used to examine association of patients' residential area/ADI with either surgery/chemotherapy or survival time, respectively. The residential area was divided into six medical areas based on the location of specialist hospitals. The medical areas were defined by Aomori Prefecture. RESULTS: The residential area (medical area) was strongly associated with access to cancer treatment for patients with NSCLC and ultimately contributed to lower survival rates. There was no significant influence in the distance from home to hospital and areal deprivation. CONCLUSION: We identified the risk factors related to a lack of medical treatment and shorter survival in rural area, Japan.

Noninvasive Computed Tomography-Based Deep Learning Model Predicts In Vitro Chemosensitivity Assay Results in Pancreatic Cancer.

OBJECTIVES: We aimed to predict in vitro chemosensitivity assay results from computed tomography (CT) images by applying deep learning (DL) to optimize chemotherapy for pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: Preoperative enhanced abdominal CT images and the histoculture drug response assay (HDRA) results were collected from 33 PDAC patients undergoing surgery. Deep learning was performed using CT images of both the HDRA-positive and HDRA-negative groups. We trimmed small patches from the entire tumor area. We established various prediction labels for HDRA results with 5-fluorouracil (FU), gemcitabine (GEM), and paclitaxel (PTX). We built a predictive model using a residual convolutional neural network and used 3-fold cross-validation. RESULTS: Of the 33 patients, effective response to FU, GEM, and PTX by HDRA was observed in 19 (57.6%), 11 (33.3%), and 23 (88.5%) patients, respectively. The average accuracy and the area under the receiver operating characteristic curve (AUC) of the model for predicting the effective response to FU were 93.4% and 0.979, respectively. In the prediction of GEM, the models demonstrated high accuracy (92.8%) and AUC (0.969). Likewise, the model for predicting response to PTX had a high performance (accuracy, 95.9%; AUC, 0.979). CONCLUSIONS: Our CT patch-based DL model exhibited high predictive performance in projecting HDRA results. Our study suggests that the DL approach could possibly provide a noninvasive means for the optimization of chemotherapy.

A Logistic Regression Model for Predicting the Risk of Subsequent Surgery among Patients with Newly Diagnosed Crohn's Disease Using a Brute Force Method.

Surgery avoidance is an important goal in Crohn's disease (CD) treatment and predicting the risk of subsequent surgery is important to determine adequate therapeutic strength for patients with newly diagnosed CD. Herein, we aimed to construct a prediction model for the risk of subsequent surgery based on disease characteristics at the patients' initial visit. We retrospectively collected disease characteristic data from 93 patients with newly diagnosed CD. A logistic regression model with a brute force method was used to maximize the area under the receiver operating characteristic curve (auROC) by employing a combination of potential predictors from 14 covariates (16,383). The auROC remained almost constant when one to 12 covariates were considered, reaching a peak of 0.89 at four covariates (small-bowel patency, extensive small-bowel lesions, main lesions, and the number of poor prognostic factors), and it decreased with increasing covariate size. The most significant predictors were small-bowel patency, extensive small-bowel lesions, and age or major lesions. Therefore, this prediction model using covariates may be helpful in determining the likelihood that a patient with newly diagnosed CD will require surgery, which can aid in appropriate treatment selection for high-risk patients.

Altered structural hippocampal intra-networks in a general elderly Japanese population with mild cognitive impairment.

Although altered networks inside the hippocampus (hippocampal intra-networks) have been observed in dementia, the evaluation of hippocampal intra-networks using magnetic resonance imaging (MRI) is challenging. We employed conventional structural imaging and incident component analysis (ICA) to investigate the structural covariance of the hippocampal intra-networks. We aimed to assess altered hippocampal intra-networks in patients with mild cognitive impairment (MCI). A cross-sectional study of 2122 participants with 3T MRI (median age 69 years, 60.9% female) were divided into 218 patients with MCI and 1904 cognitively normal older adults (CNOA). By employing 3D T1-weighted imaging, voxels within the hippocampus were entered into the ICA analysis to extract the structural covariance intra-networks within the hippocampus. The ICA extracted 16 intra-networks from the hippocampal structural images, which were divided into two bilateral networks and 14 ipsilateral networks. Of the 16 intra-networks, two (one bilateral network and one ipsilateral networks) were significant predictors of MCI from the CNOA after adjusting for age, sex, education, disease history, and hippocampal volume/total intracranial volume ratio. In conclusion, we found that the relationship between hippocampal intra-networks and MCI was independent from the hippocampal volume. Our results suggest that altered hippocampal intra-networks may reflect a different pathology in MCI from that of brain atrophy.

A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging.

Narrow band imaging (NBI) has been extensively utilized as a diagnostic tool for colorectal neoplastic lesions. This study aimed to develop a trial deep learning (DL) based four-class classification model for low-grade dysplasia (LGD); high-grade dysplasia or mucosal carcinoma (HGD); superficially invasive submucosal carcinoma (SMs) and deeply invasive submucosal carcinomas (SMd) and evaluate its potential as a diagnostic tool. We collected a total of 1,390 NBI images as the dataset, including 53 LGD, 120 HGD, 20 SMs and 17 SMd. A total of 598,801 patches were trimmed from the lesion and background. A patch-based classification model was built by employing a residual convolutional neural network (CNN) and validated by three-fold cross-validation. The patch-based validation accuracy was 0.876, 0.957, 0.907 and 0.929 in LGD, HGD, SMs and SMd, respectively. The image-level classification algorithm was derived from the patch-based mapping across the entire image domain, attaining accuracies of 0.983, 0.990, 0.964, and 0.992 in LGD, HGD, SMs, and SMd, respectively. Our CNN-based model demonstrated high performance for categorizing the histological grade of dysplasia as well as the depth of invasion in routine colonoscopy, suggesting a potential diagnostic tool with minimal human inputs.

Association of prediabetes with reduced brain volume in a general elderly Japanese population.

OBJECTIVES: Diabetes frequently results in cognitive impairment, but it is less clear if brain health is adversely affected during the prediabetic stage. Our aim is to identify possible changes in brain volume as measured by magnetic resonance imaging (MRI) in a large elderly population stratified according to level of "dysglycemia." METHODS: This is a cross-sectional study of 2144 participants (median age 69 years, 60.9% female) who underwent 3-T brain MRI. Participants were divided into 4 dysglycemia groups based on HbA1c levels (%): normal glucose metabolism (NGM) (< 5.7%), prediabetes (5.7 to < 6.5%), undiagnosed diabetes (6.5% or higher), and known diabetes (defined by self-report). RESULTS: Of the 2144 participants, 982 had NGM, 845 prediabetes, 61 undiagnosed diabetes, and 256 known diabetes. After adjustment for age, sex, education, body weight, cognitive status, smoking, drinking, and disease history, total gray matter volume was significantly lower among participants with prediabetes (0.41% lower, standardized ß = - 0.0021 [95% CI - 0.0039, - 0.00039], p = 0.016), undiagnosed diabetes (1.4% lower, standardized ß = - 0.0069 [95% CI - 0.012, - 0.002], p = 0.005), and known diabetes (1.1% lower, standardized ß = - 0.0055 [95% CI - 0.0081, - 0.0029], p < 0.001) compared to the NGM group. After adjustment, total white matter volume and hippocampal volume did not differ significantly between the NGM group and either the prediabetes group or the diabetes group. CONCLUSION: Sustained hyperglycemia may have deleterious effects on gray matter integrity even prior to the onset of clinical diabetes. KEY POINTS: • Sustained hyperglycemia has deleterious effects on gray matter integrity even prior to the onset of clinical diabetes.

Differential diagnoses of gallbladder tumors using CT-based deep learning.

Background: The differential diagnosis between gallbladder cancer (GBC) and xanthogranulomatous cholecystitis (XGC) remains quite challenging, and can possibly lead to improper surgery. This study aimed to distinguish between XGC and GBC by combining computed tomography (CT) images and deep learning (DL) to maximize the therapeutic success of surgery. Methods: We collected a dataset, including preoperative CT images, from 28 cases of GBC and 21 XGC patients undergoing surgery at our facility. It was subdivided into training and validation (n = 40), and test (n = 9) datasets. We built a CT patch-based discriminating model using a residual convolutional neural network and employed 5-fold cross-validation. The discriminating performance of the model was analyzed in the test dataset. Results: Of the 40 patients in the training dataset, GBC and XGC were observed in 21 (52.5%), and 19 (47.5%) patients, respectively. A total of 61 126 patches were extracted from the 40 patients. In the validation dataset, the average sensitivity, specificity, and accuracy were 98.8%, 98.0%, and 98.5%, respectively. Furthermore, the area under the receiver operating characteristic curve (AUC) was 0.9985. In the test dataset, which included 11 738 patches, the discriminating accuracy for GBC patients after neoadjuvant chemotherapy (NAC) (n = 3) was insufficient (61.8%). However, the discriminating model demonstrated high accuracy (98.2%) and AUC (0.9893) for cases other than those receiving NAC. Conclusion: Our CT-based DL model exhibited high discriminating performance in patients with GBC and XGC. Our study proposes a novel concept for selecting the appropriate procedure and avoiding unnecessary invasive measures.

Difference in net survival using regional and national life tables in Japan.

BACKGROUND: National life table is commonly used for estimating cancer net survival. However, the national life table does not reflect condition of people in local area accurately, because there are disparities in cancer mortality rates among the local area in many cases. We investigated magnitude of difference in cancer net survival using the local area in Japan and Japanese life tables. METHODS: We analyzed data from 32,942 cancer patients diagnosed between 2010 and 2012 in Aomori prefecture, Japan. Expected survival rates in Aomori (ESA) and Japan (ESJ) were estimated based on the life table of each area. Five-year net survival rates using ESA and the ESJ were estimated using the Pohar-Perme method. RESULTS: The difference between net survival rates using the ESA (NSA) and the ESJ (NSJ) were larger than in men (0.3－3.0%) than in women (0.1－0.8%). The largest difference in the net survival rate was observed in prostate cancer patients, because the difference in the expected survival in oldest old men was remarkable. CONCLUSION: Two factors affected the difference in the net survival rates resulting from the sensitivity analysis. The difference was larger (1) among older patients or (2) with a longer observation period (person-years).

Clinical Features of Fecal Immunochemical Test-Negative Colorectal Lesions based on Colorectal Cancer Screening among Asymptomatic Participants in Their 50s.

OBJECTIVE: To improve the efficacy of colorectal cancer (CRC) screening, decreasing the occurrence of interval cancers is essential. Most interval CRCs develop from fecal immunochemical test (FIT)-negative CRC. This study examined the clinical characteristics of FIT-negative advanced neoplasms (AN) and sessile serrated lesions (SSL), which are main candidate precursors of FIT-negative CRC, and the eligibility criteria for total colonoscopy (TCS) screening following negative FIT. METHODS: Asymptomatic participants in their 50s were divided into two groups. The FIT-negative group underwent TCS following negative FIT, and the TCS-only group underwent TCS without FIT. One endoscopist reviewed the endoscopic images. Plausible risk factors for colorectal polyps were extracted. The clinical features of AN and SSL were compared between the groups. RESULT: Of 2,437 participants, 56.2% were included in the FIT-negative group. No between-group differences were recorded for the prevalence of different colorectal polyp types. By multivariate analysis, a significantly lower adjusted odds ratio (AOR) of AN was shown in women, and significantly higher AORs of AN were found for aging, smoking, and a family history of CRC. The AOR of SSL was higher for smokers. The proportion of AN in the right colon was higher in the FIT-negative group. No between-group differences were recorded for SSL. CONCLUSION: FIT screening was less likely to detect CRC and certain precancerous lesions in the right colon. Combining annual FIT with TCS for the high-risk population based on a scoring system, may detect FIT-negative CRC and colorectal polyps, thus, reducing interval cancer.

Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50.

Capsule endoscopy has been widely used as a non-invasive diagnostic tool for small or large intestinal lesions. In recent years, automated lesion detection systems using machine learning have been devised. This study aimed to develop an automated system for capsule endoscopic severity in patients with ulcerative colitis along the entire length of the colon using ResNet50. Capsule endoscopy videos from patients with ulcerative colitis were collected prospectively. Each single examination video file was partitioned into four segments: the cecum and ascending colon, transverse colon, descending and sigmoid colon, and rectum. Fifty still pictures (576 × 576 pixels) were extracted from each partitioned video. A patch (128 × 128 pixels) was trimmed from the still picture at every 32-pixel-strides. A total of 739,021 patch images were manually classified into six categories: 0) Mayo endoscopic subscore (MES) 0, 1) MES1, 2) MES2, 3) MES3, 4) inadequate quality for evaluation, and 5) ileal mucosa. ResNet50, a deep learning framework, was trained using 483,644 datasets and validated using 255,377 independent datasets. In total, 31 capsule endoscopy videos from 22 patients were collected. The accuracy rates of the training and validation datasets were 0.992 and 0.973, respectively. An automated evaluation system for the capsule endoscopic severity of ulcerative colitis was developed. This could be a useful tool for assessing topographic disease activity, thus decreasing the burden of image interpretation on endoscopists.

Association of serum creatinine-to-cystatin C ratio with skeletal muscle mass and strength in nonalcoholic fatty liver disease in the Iwaki Health Promotion Project.

We evaluated the feasibility of using serum creatinine-to-cystatin C ratio in the assessments of muscle mass and strength in nonalcoholic fatty liver disease. In a community-based cross-sectional study, skeletal muscle mass and handgrip strength were assessed in 641 Japanese adults. Low skeletal muscle mass index and low handgrip strength were defined as indicated in the sarcopenia diagnostic criteria of the Japan Society of Hepatology. Nonalcoholic fatty liver disease was defined as fatty liver on ultrasonography in the absence of other causes of steatosis. The creatinine-to-cystatin C ratio was useful for identifying the participants with low skeletal muscle mass index, with an area under the receiver-operating characteristic curve of 0.84 [95% confidence interval (CI), 0.77-0.91] in men and 0.72 in women (95% CI, 0.65-0.78), and those with low handgrip strength, with an area under the receiver-operating characteristic curve of 0.96 (95% CI, 0.93-0.99) in men and 0.79 (95% CI, 0.66-0.92) in women. Moreover, the creatinine-to-cystatin C ratio correlated with skeletal muscle mass index (r = 0.511, p<0.001) and handgrip strength (r = 0.657, p<0.001), whereas it did not correlate with exacerbation of hepatic steatosis. In this study, creatinine-to-cystatin C ratio correlated with muscle mass and strength in nonalcoholic fatty liver disease regardless of hepatic steatosis.

CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma.

Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recurrence through the use of preoperative images. We collected the dataset, including preoperative plain computed tomography (CT) images, from 41 patients undergoing curative surgery for iCCA at multiple institutions. We built a CT patch-based predictive model using a residual convolutional neural network and used fivefold cross-validation. The prediction accuracy of the model was analyzed. We defined early recurrence as recurrence within a year after surgical resection. Of the 41 patients, early recurrence was observed in 20 (48.8%). A total of 71,081 patches were extracted from the entire segmented tumor area of each patient. The average accuracy of the ResNet model for predicting early recurrence was 98.2% for the training dataset. In the validation dataset, the average sensitivity, specificity, and accuracy were 97.8%, 94.0%, and 96.5%, respectively. Furthermore, the area under the receiver operating characteristic curve was 0.994. Our CT-based DL model exhibited high predictive performance in projecting postoperative early recurrence, proposing a novel insight into iCCA management.

Clinicopathological risk factors for a poor prognosis of primary central nervous system lymphoma in elderly patients in the Tohoku and Niigata area: a multicenter, retrospective, cohort study of the Tohoku Brain Tumor Study Group.

Clinicopathological risk factors for a poor prognosis were investigated in elderly patients with malignant lymphoma of the central nervous system. A total of 82 pathologically confirmed, CD20-positive, diffuse large B-cell lymphoma patients aged 71 years or older who underwent therapeutic intervention in the Tohoku and Niigata area in Japan were retrospectively reviewed. A univariate analysis was performed by the log-rank test using the Kaplan-Meier method. A Cox proportional hazards model was used for multivariate analysis of risk factors. Of the 82 patients, 39 were male and 43 were female, and their median age at onset was 75 years. At the end of the study, there were 34 relapse-free patients (41.5%), 48 relapse cases (58.5%), median progression-free survival was 18 months, and median overall survival (OS) was 26 months; there were 41 deaths and 41 survivors. Multivariate analysis of median OS showed that Karnofsky Performance Status less than 60% 3 months after treatment (p = 0.022, hazard ratio (HR) = 2.591) was the clinical risk factor, and double expressor lymphoma (p = 0.004, HR = 3.163), expression of programmed death-ligand 1 in tumor infiltrating lymphocytes or tumor-associated macrophages (p < 0.001, HR = 5.455), and Epstein-Barr virus infection (p = 0.031, HR = 5.304) were the pathological risk factors.

Prognosis by cancer type and incidence of zoledronic acid-related osteonecrosis of the jaw: a single-center retrospective study.

PURPOSE: Survival time after bisphosphonate use has been increasingly recognized to be associated with the incidence of medication-related osteonecrosis of the jaw (MRONJ); however, this has not been elucidated sufficiently in the literature. This study aimed to clarify the incidence of MRONJ and the corresponding survival rate of patients treated with zoledronic acid (ZA) for each type of cancer and obtain useful information for the oral/dental supportive care of cancer patients. METHODS: We evaluated 988 patients who were administered ZA at our hospital; among them, 862 patients with metastatic bone tumors or myeloma were included. RESULTS: The median survival time (MST) after ZA initiation was 35, 34, 8, 41, 12, and 6 months for patients with breast, prostrate, lung, myeloma, renal, and other cancers, respectively. Patients with cancers that had a short survival time (lung and other cancers [MST = 8 and 6 months, respectively] and cancers with MST < 10 months) did not develop MRONJ; this could be attributed to the shorter duration of ZA administration. The cumulative incidence of MRONJ in breast cancer, prostate cancer, and multiple myeloma was related to the frequency of anti-resorptive drug use and the increased risk over time. In renal cancer, the cumulative incidence of MRONJ increased early, although the MST was 12 months. CONCLUSION: For the dentists in charge of dental management, it is essential to be aware of prognosis-related factors, predict MRONJ risk for each cancer treatment, and use risk prediction in dental management planning, particularly for cancers with non-poor prognosis.

Effectiveness of Cytological Diagnosis with Outer Cannula Washing Solution for Computed Tomography-Guided Needle Biopsy.

RATIONALE AND OBJECTIVES: We evaluated the availability of cytological diagnosis with outer cannula washing solution (C-OCWS) as a clinical diagnostic tool for computed tomography (CT)-guided needle biopsy. MATERIALS AND METHODS: We retrospectively assessed 109 consecutive patients (71 males, 38 females; median age 68 years), who underwent CT-guided needle biopsy. In all patients, the specimens sampled by the inner needle were used for histological diagnosis, and those taken from the outer cannula were rinsed with 0.9% saline solution: outer cannula washing solution for cytological diagnosis. The accuracy of C-OCWS in addition to histological diagnosis were compared with that of histological diagnosis alone. We used binary logistic regression analysis to determine the variables associated with diagnostic accuracy for malignancy and lesion characteristics. RESULTS: The C-OCWS method precisely diagnosed 7 (6.4%) malignant lesions (i.e., effective cases) in the 109 patients characterized as "negative for malignancy" via histological diagnosis alone. The accuracy of the combination of C-OCWS and histological diagnoses was significantly higher than that of histological diagnosis alone (0.95 vs. 0.89, respectively; p = 0.023). Multivariate logistic regression analysis showed that increasing only a marginal ratio (failure rate for proper position of biopsy needle within the tumor) was independently associated with a high rate of effective cases (p = 0.003). CONCLUSION: C-OCWS may be helpful for improving the quality of CT-guided needle biopsy, and is a simple method that may not necessarily increase the patients' physical burden.

Brain tumor-related epilepsy and risk factors for metastatic brain tumors: analysis of 601 consecutive cases providing real-world data.

OBJECTIVE: It is necessary to accurately characterize the epidemiology and trends of brain tumor-related epilepsy (BTE) in patients with metastatic brain tumors. This study aimed to determine the incidence of BTE associated with metastatic brain tumors and retrospectively investigate the risk factors for BTE. METHODS: This retrospective analysis included 601 of 631 consecutive patients with metastatic brain tumors who received treatment, including surgery, radiotherapy, and/or other treatments. BTE and the clinical course were examined retrospectively. Logistic regression multivariate analyses were performed to identify risk factors for BTE. RESULTS: BTE was reported in 148 (24.6%) of 601 patients during the entire course. Of these 148 patients, 81 (54.7%) had first-onset epilepsy (13.5% of all patients). Of the 520 cases of nonepileptic onset, 53 were in the prophylactic antiepileptic drug (AED) group. However, 12 of these patients and 55 of the no-prophylactic AED group developed epilepsy during the course of the study. Including these 67 patients, 148 patients were examined as the group of all epilepsy cases during the entire course. In 3 patients, the seizure progressed to status epilepticus. In most patients, the BTE (n = 83, 56.1%) manifested as focal aware seizures. Logistic regression analysis identified young age (p = 0.037), male sex (p = 0.026), breast cancer (p = 0.001), eloquent area (p < 0.001), peritumoral edema (p < 0.001), dissemination (p = 0.013), and maximum tumor volume (p = 0.021) as significant risk factors for BTE. BTE was more common with tumor volumes greater than the cutoff value of 1.92 ml. CONCLUSIONS: BTE appears to be more likely to occur in cases with young age, male sex, breast cancer, tumors involving eloquent areas, brain edema, dissemination, and giant tumors.

The Real-World status and risk factors for a poor prognosis in elderly patients with primary central nervous system malignant lymphomas: a multicenter, retrospective cohort study of the Tohoku Brain Tumor Study Group.

BACKGROUND: Elderly patients with primary central nervous system malignant lymphoma (EL-PCNSL) may not be given sufficient treatment due to their poor pre-treatment Karnofsky Performance Status (KPS) and comorbidities. Therefore, a retrospective, cohort study was performed to evaluate risk factors associated with a poor prognosis of EL-PCNSL in the Tohoku Brain Tumor Study Group. METHODS: Patients aged ≥ 71 years with PCNSL were enrolled from eight centers. Univariate analysis was performed with the log-rank test. A Cox proportional hazards model was used for multivariate analysis. RESULTS: Three of the total 142 cases received best supportive care (BSC). Treatment was given to 30 cases without a pathological diagnosis, 3 cases with cerebrospinal fluid (CSF) cytology, and 100 cases with a pathological diagnosis. After confirmation of no differences in progression-free survival (PFS) and overall survival (OS) between the group treated without pathology and the groups diagnosed by pathology or CSF cytology and between median age ≥ 76 years and < 76 years, a total of 133 patients were studied. The median pre-treatment KPS was 50%. Median PFS and median OS were 16 and 24 months, respectively. Risk factors associated with poor prognosis on Cox proportional hazards model analysis were pre-treatment cardiovascular disease and central nervous system disease comorbidities, post-treatment pneumonia and other infections, and the absence of radiotherapy or chemotherapy. CONCLUSIONS: Pre-treatment comorbidities and post-treatment complications would affect the prognosis. Radiation and chemotherapy were found to be effective, but no conclusions could be drawn regarding the appropriate content of chemotherapy and whether additional radiotherapy should be used.