Diagnosis System For Classification Research Articles

Sivan classification system for diagnosis of jaw lesions based on visual volumetric analysis of 3-dimensional cone-beam computed tomographic images

A novel classification system, termed the Sivan classification, was developed to enhance the diagnosis of jaw lesions by utilizing visual volumetric analysis of three-dimensional Cone Beam Computed Tomography (CBCT) images. This classification groups lesions into ten categories, primarily divided into hypovolumetric, hypervolumetric, and normovolumetric groups. To validate this system, 10 raters—comprising 5 general dentists and 5 oral radiology specialists—assessed the CBCT images and diagnosed the lesions using the Sivan classification. Eight raters repeated the process after one month to assess consistency. The overall agreement between raters, quantified using kappa statistics, was 0.82, indicating excellent consistency. Hypervolumetric and normovolumetric lesions demonstrated the highest agreement (kappa 0.84 and 0.82, respectively), while hypovolumetric lesions showed substantial agreement (kappa 0.77). Pairwise interrater agreement ranged from 76 to 93%, with kappa values between 0.75 and 0.87. Intrarater reliability was equally strong, with kappa values between 0.79 and 0.89.These results suggest that the Sivan classification provides a robust and reliable framework for diagnosing jaw lesions using CBCT volumetric analysis, surpassing traditional diagnostic methods in accuracy and consistency.

Brazilian classification of physical therapy diagnosis

BackgroundThe Brazilian Classification of Physical Therapy Diagnosis, developed by the Federal Council of Physiotherapy and Occupational Therapy (COFFITO), has the constitutional objectives of standardizing ethical, scientific and social aspects of the Physical Therapy profession. ObjectiveTo describe the work process, rationale and proposal for standardization of a physical therapy diagnosis classification system in Brazil. MethodsA working group was created to propose a standardized classification for the description and codification of physical therapy diagnoses. Some terminologies common to the International Classification of Functioning (ICF) were used to make the nomenclature of diagnoses compatible with the outcomes inherent in the field of physical therapy. ResultsThe Brazilian Classification of Physical Therapy Diagnosis project culminated in a physical therapy diagnosis model consisting of terms grouped by organic systems and identifying codes. In addition, an application was developed to allow professionals to use the standardized diagnostic classification in an online system. ConclusionThe diagnostic classification system is expected to advance the Physical Therapy profession allowing the identification of structural and/or functional alterations in a simplified and standardised manner. From a physical therapy perspective, this classification may help to consolidate the autonomy of the Brazilian physical therapists by establishing a clearer pathway between the diagnosis and interventions.

Catalogues of EQ-5D-3L Health-Related Quality of Life Scores for 199 Chronic Conditions and Health Risks for Use in the UK and the USA.

Health-related quality of life (HRQoL) measures are essential in economic evaluation, but sometimes primary sources are unavailable, and information from secondary sources is required. Existing HRQoL UK/US catalogues are based on earlier diagnosis classification systems, amongst other issues. A recently published Danish catalogue merged EQ-5D-3L data from national health surveys with national registers containing patient information on ICD-10 diagnoses, healthcare activities and socio-demographics. To provide (1) UK/US EQ-5D-3L-based HRQoL utility population catalogues for 199 chronic conditions on the basis of ICD-10 codes and health risks and (2) regression models controlling for age, sex, comorbidities and health risks to enable predictions in other populations. UK and US EQ-5D-3L value sets were applied to the EQ-5D-3L responses of the Danish dataset and modelled using adjusted limited dependent variable mixture models (ALDVMMs). Unadjusted mean utilities, percentiles and adjusted disutilities based on two ALDVMMs with different control variables were provided for both countries. Diseases from groups M, G, and F consistently had the smallest utilities and the largest negative disutilities: fibromyalgia (M797), sclerosis (G35), rheumatism (M790), dorsalgia (M54), cerebral palsy (G80-G83), post-traumatic stress disorder (F431), dementia (F00-2), and depression (F32, etc.). Risk factors, including stress, loneliness, and BMI30+, were also associated with lower HRQoL. This study provides comprehensive catalogues of UK/US EQ-5D-3L HRQoL utilities. Results are relevant in cost-effectiveness analysis, for NICE submissions, and for comparing and identifying facets of disease burden.

Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture

Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms. An ophthalmologist often makes the diagnosis of eye-related diseases by analyzing fundus images to identify the stages and symptoms of HR. The likelihood of vision loss can significantly decrease the initial detection of HR. In the past, a few computer-aided diagnostics (CADx) systems were developed to automatically detect HR eye-related diseases using machine learning (ML) and deep learning (DL) techniques. Compared to ML methods, the CADx systems use DL techniques that require the setting of hyperparameters, domain expert knowledge, a huge training dataset, and a high learning rate. Those CADx systems have shown to be good for automating the extraction of complex features, but they cause problems with class imbalance and overfitting. By ignoring the issues of a small dataset of HR, a high level of computational complexity, and the lack of lightweight feature descriptors, state-of-the-art efforts depend on performance enhancement. In this study, a pretrained transfer learning (TL)-based MobileNet architecture is developed by integrating dense blocks to optimize the network for the diagnosis of HR eye-related disease. We developed a lightweight HR-related eye disease diagnosis system, known as Mobile-HR, by integrating a pretrained model and dense blocks. To increase the size of the training and test datasets, we applied a data augmentation technique. The outcomes of the experiments show that the suggested approach was outperformed in many cases. This Mobile-HR system achieved an accuracy of 99% and an F1 score of 0.99 on different datasets. The results were verified by an expert ophthalmologist. These results indicate that the Mobile-HR CADx model produces positive outcomes and outperforms state-of-the-art HR systems in terms of accuracy.

Comparison of the Predictive Performance of Medical Coding Diagnosis Classification Systems

Health analytics frequently involve tasks to predict outcomes of care. A foundational predictor of clinical outcomes is the medical diagnosis (Dx). The most used expression of medical Dx is the International Classification of Diseases (ICD-10-CM). Since ICD-10-CM includes >70,000 codes, it is computationally expensive and slow to train models with. Alternative lower-dimensionality alternatives include clinical classification software (CCS) and diagnosis-related groups (MS-DRGs). This study compared the predictive power of these alternatives against ICD-10-CM for two outcomes of hospital care: inpatient mortality and length of stay (LOS). Naïve Bayes (NB) and Random Forests models were created for each Dx system to examine their predictive performance for inpatient mortality, and Multiple Linear Regression models for the continuous LOS variable. The MS-DRGs performed highest for both outcomes, even outperforming ICD-10-CM. The admitting ICD-10-CM codes were, surprisingly, not underperformed by the primary ICD-10-CM Dxs. The CCS system, although having a much lower dimensionality than ICD-10-CM, has only slightly lower performance while the refined version of CCS only slightly outperformed the old CCS. Random Forests outperformed NB for MS-DRG, and ICD-10-CM, by a large margin. Results can provide insights to understand the compromise from using lower-dimensionality representations in clinical outcome studies.

Covid-19 risk factors: statistical learning from German healthcare claims data

Background Precise individual risk quantification of severe courses of Covid-19 is needed to prioritize protective measures and to assess population risks in a phase of increased immunization. So far, results for the German population are lacking. Furthermore, existing studies pre-specify comorbidity risks by broad categories rather than deriving them from the data using statistical learning techniques. Methods Risk factors for severe, critical and lethal courses of Covid-19 are identified from a large German claims dataset covering more than 4 million individuals. To avoid prior grouping and pre-selection of risk factors, fine-grained hierarchical information from medical classification systems for diagnoses, pharmaceuticals and procedures are used, resulting in more than 33,000 covariates. These are processed using a LASSO approach. Results We identify relevant risk factors, among which hypertensive diseases, heart disease and the corresponding medications are most relevant at population level. Prior use of diuretics is the strongest single medical predictor for severe course (e.g. Torasemide, odds ratio (OR) 1.801), but also for a critical course (OR 2.304) and death (OR 2.523). To assess risk profiles at the individual level, our approach sums up many such factors and has better predictive ability than using pre-specified morbidity groups (AUC for predicting critical course 0.875 versus AUC ≤ 0.865). Conclusions The proposed method can help to identify risk factors and assess risk at the individual level for other infectious diseases. The results can be used by administrative data holders to guide protective policies, while a risk index can be applied in clinical studies with a narrower focus.

Effectiveness of TI-RADS and ATA classifications for predicting malignancy of thyroid nodules.

Thyroid cancer is one of the most common cancers and is especially common in young patients. Therefore, effective recognition and treatment of thyroid cancer are essential for patient survival. To compare the effectiveness of standard guidelines for predicting thyroid malignancy. To do so, thyroid nodules were classified according to the categories of the American Thyroid Association (ATA) and Thyroid Imaging Reporting and Data System (TI-RADS) guidelines, and compared with fine-needle aspiration biopsy (FNAB) results. The study included 1741 thyroid nodules with a final diagnosis in 1121 consecutive patients. The FNAB was recommended for all patients according to ATA guidelines and subsequently performed. The nodules were reclassified according to TI-RADS guidelines. Comparing nodules classified according to ATA and TI-RADS in terms of ultrasonography (US) features with the Bethesda cytological diagnosis classification System for Reporting Thyroid Cytopathology, 37.6% of the nodules classified in the high-risk category according to the ATA classification were found to be malignant cytology, 10.4% suspicious for malignancy, 4% non-diagnostic, 9.6% indeterminant cytology, and 38.4% benign. According to the TI-RADS risk category, 50% of those with high suspicion were malignant, 13.3% suspicious for malignancy cytology and 36.7% were benign. For the TI-RADS guidelines, the best cutoff value for differentiating benign and malignant nodules was found to be 4.5 (area under the curve (AUC) = 0.962, 95% CI = 0.943-0.981, p < 0.001). For the ATA guidelines, the best cutoff value for separating benign and malignant nodules was 4.5 (AUC = 0.917, 95% CI = 0.875-0.959, p < 0.001). The diagnostic performances of the TI-RADS and ATA score systems were evaluated using highly suspicious nodules. The sensitivity and specificity of highly suspicious nodules, according to both TI-RADS and ATA guidelines, were both high. Sensitivity and specificity of ATA classification were 80% and 96.3%, respectively. Sensitivity and specificity of TI-RADS classification were 76% and 97.5%, respectively, but positive predictive value was low (63.3% compared to 55.5%). Both, the ATA and TI-RADS classifications can effectively predict malignancy risk of thyroid nodules and may thus decrease unnecessary FNAB.

Clinical validation of the SIMPLE classification for optical diagnosis of colorectal polyps

Background and study aims A novel endoscopic optical diagnosis classification system (SIMPLE) has recently been developed. This study aimed to evaluate the SIMPLE classification in a clinical cohort. Patients and methods All diminutive and small colorectal polyps found in a cohort of individuals undergoing screening, diagnostic, or surveillance colonoscopies underwent optical diagnosis using image-enhanced endoscopy (IEE) and the SIMPLE classification. The primary outcome was the agreement of surveillance intervals determined by optical diagnosis compared with pathology-based results for diminutive polyps. Secondary outcomes included the negative predictive value (NPV) for rectosigmoid adenomas, the percentage of pathology exams avoided, and the percentage of immediate surveillance interval recommendations. Analysis of optical diagnosis for polyps ≤ 10 mm was also performed. Results 399 patients (median age 62.6 years; 55.6 % female) were enrolled. For patients with at least one polyp ≤ 5 mm undergoing optical diagnosis, agreement with pathology-based surveillance intervals was 93.5 % (95 % confidence interval [CI] 91.4–95.6). The NPV for rectosigmoid adenomas was 86.7 % (95 %CI 77.5–93.2). When using optical diagnosis, pathology analysis could be avoided in 61.5 % (95 %CI 56.9–66.2) of diminutive polyps, and post-colonoscopy surveillance intervals could be given immediately to 70.9 % (95 %CI 66.5–75.4) of patients. For patients with at least one ≤ 10 mm polyp, agreement with pathology-based surveillance intervals was 92.7 % (95 %CI 89.7–95.1). NPV for rectosigmoid adenomas ≤ 10 mm was 85.1 % (95 %CI CI 76.3–91.6). Conclusions IEE with the SIMPLE classification achieved the quality benchmark for the resect and discard strategy; however, the NPV for rectosigmoid polyps requires improvement.

Depiction of cardiovascular surgery in the current modified German DRG system 2021

EinleitungDas Jahr 2020 wird immer im Schatten der durch die „coronavirus disease 2019“ (COVID-19) ausgelösten Pandemie stehen. Die ersten 3 Quartale 2020 waren geprägt von einem beispiellosen Herunterfahren der elektiven stationären Leistungserbringung und einem Freihalten von Intensivkapazitäten für die Behandlung von COVID-19-Patienten. Auch für die Herzchirurgie resultierten dadurch deutliche Auswirkungen auf die Leistungserbringung. Die Akteure in den Kliniken waren im Jahresverlauf zusätzlich mit einer Fülle von Änderungen der gesetzlichen Rahmenbedingungen konfrontiert. Dennoch wurde das aG-DRG-System 2021 durch das Institut für das Entgeltsystem im Krankenhaus (InEK) kalkuliert. Dieser Beitrag beschreibt und bewertet die wesentlichen Veränderungen des aG-DRG-Systems 2021 für die Herz-Thorax- und Gefäßchirurgie.MethodikAnalyse der relevanten Diagnosen, Prozeduren und G‑DRG-Strukturen in den Systemversionen 2020 und 2021 anhand der durch InEK und das Bundesinsitut für Arzneimittel und Medizinprodukte (BfArM) publizierten Informationen.ErgebnisseErweiterungen der relevanten Klassifikationssysteme für Diagnosen (ICD-10-GM 2021) und Prozeduren (OPS 2021) führen zu einer Zunahme spezifischerer Kodierungen von wesentlichen Interventionen und Operationen der Herz- und Gefäßchirurgie. Im Rahmen der Anpassung der G‑DRG-Strukturen sind besonders die Kondensation der bisherigen Fallpauschalen für die Herztransplantation zur aG-DRG A05Z und Abwertungen koronarer Bypassoperationen und rekonstruktiver Gefäßeingriffe zu nennen.SchlussfolgerungFür die Herz- und Gefäßchirurgie finden sich vielfältige Veränderungen mit z. T. erheblichen Auswirkungen auf die Fallerlöse. Hinzu kommt, dass die Auswirkungen der „Coronapandemie“ für viele deutsche Kliniken noch nicht abschließend absehbar sind. Ein weiterer, immer dringlicherer insbesondere die Gefäßmedizin betreffende Einflussfaktor ist der zunehmende Druck zur Ambulantisierung von Leistungen. Hier wird der im Jahre 2022 erwartete und voraussichtlich deutlich erweiterte Katalog für Ambulantes Operieren im Krankenhaus (AOP-Katalog) den Zwang zur ambulanten Erbringung bisher noch stationär erbrachter Leistungen sicher noch einmal deutlich erhöhen.

Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses.

The American College of Radiology (ACR) recently published the ovarian-adnexal reporting and data system (O-RADS) to provide guidelines to physicians who interpret ultrasound (US) examinations of adnexal masses (AM). This study aimed to compare the O-RADS with two other well-established US classification systems for diagnosis of AM. This retrospective multicenter study between May 2016 and December 2019 assessed consecutive women with AM detected by the US. Five experienced consultant radiologists independently categorized each AM according to O-RADS, gynecologic imaging reporting and data system (GI-RADS), and international ovarian tumor analysis (IOTA) simple rules. Pathology and adequate follow-up were used as reference standards for calculating the validity of three US classification systems for diagnosis of AM. Kappa statistics were used to assess the inter-reviewer agreement (IRA). A total of 609 women (mean age, 48 ± 13.7 years; range, 18-72 years) with 647 AM were included. Of the 647 AM, 178 were malignant and 469 were benign. Malignancy rates were comparable to recommended rates by previous literature in O-RADS and IOTA, but higher in GI-RADS. O-RADS had significantly higher sensitivity for malignancy than GI-RAD and IOTA (p = 0.003 and 0.0007, respectively), but non-significant slightly lower specificity (p > 0.05). O-RADS, GI-RADS, and IOTA showed similar overall IRA (κ = 0.77, 0.69, and 0.63, respectively) with a tendency toward higher IRA with O-RADS than with GI-RADS and IOTA. O-RADS compares favorably with GI-RADS and IOTA. O-RADS had higher sensitivity than GI-RADS and IOTA simple rules with relatively similar specificity and reliability. • The malignancy rates were comparable to recommended rates by previous literature in O-RADS and IOTA, but higher in GI-RADS. • The O-RADS had significantly higher sensitivity for malignancy than GI-RADS and IOTA (96.8% vs 92.7% and 92.1%; p = 0.003 and 0.0007, respectively), but non-significant slightly lower specificity (92.8% vs 93.6% and 93.2%, respectively; p > 0.05). • The O-RADS, GI-RADS, and IOTA showed similar overall inter-reviewer agreement (IRA) (κ = 0.77, 0.69, and 0.63, respectively), with a tendency toward higher IRA with O-RADS than with GI-RADS and IOTA.

Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification

On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance. Diagnoses were mapped using 3 common diagnostic classification systems: World Health Organization (WHO) disease chapters, Department of Health and Human Services Hierarchical Condition Categories (HHS-HCCs), and Agency for Healthcare Research and Quality Clinical Classification System (AHRQ-CCS). Data were analyzed from December 1, 2018, to January 21, 2020. US implementation of ICD-10-CM. Monthly rates of individuals with at least 1 diagnosis in a diagnostic classification category per 10 000 eligible members. The analytic sample contained information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnoses; the mean (range) monthly sample size was 22.1 (18.4 to 27.1 ) million individuals. While diagnostic category prevalence changed minimally for WHO disease chapters, the ICD-10-CM transition was associated with level changes of 20% or more among 20 of 127 HHS-HCCs (15.7%) and 46 of 282 AHRQ-CCS categories (16.3%) and with trend changes of 20% or more among 12 of 127 of HHS-HCCs (9.4%) and 27 of 282 of AHRQ-CCS categories (9.6%). For HHS-HCCs, monthly rates of individuals with any acute myocardial infarction diagnosis increased 131.5% (95% CI, 124.1% to 138.8%), primarily because HHS added non-ST-segment-elevation myocardial infarction diagnoses to this category. The HHS-HCC for diabetes with chronic complications increased by 92.4% (95% CI, 84.2% to 100.5%), primarily from including new diabetes-related hypoglycemia and hyperglycemia codes, and the rate for completed pregnancy with complications decreased by 54.5% (95% CI, -58.7% to -50.2%) partly due to removing vaginal birth after cesarean delivery as a complication. These findings suggest that the ICD-10-CM transition was associated with large prevalence changes for many diagnostic categories. Diagnostic classification systems developed using ICD-9-CM may need to be refined using ICD-10-CM data to avoid unintended consequences for disease surveillance, performance assessment, and risk-adjusted payments.

Temporomandibular disorders in head and neck cancers: Overview of specific mechanisms and management

Diagnosis classification system of Temporomandibular disorders (TMD) is based on the biopsychosocial model of pain. The pathogenesis is poorly understood, leading to difficulties in treating these multifactorial conditions. The predisposing factors are pathophysiological, psychological or structural processes that alter the masticatory system and lead to an increase in the risk of development of TMD. The purpose of this integrative review was then to point out the specific mechanisms of TMD in the oral oncologic context to optimize the TMJ functional results in the management of patients with oral oncologic conditions. We explored in this paper the role of Axis II assessment of the biopsychosocial model of pain, the involvement of mechanical concepts such as dental occlusion, mandibular condyle positioning and related-structures reconstruction, and the stomatognathic changes induced by radiation.

Fuzzy Classification with Comprehensive Learning Gravitational Search Algorithm in Breast Tumor Detection

The research paper herewith presents an effectual diagnosis classification system using fuzzy classifier and a very efficient heuristics algorithm comprehensive learning gravitational search algorithm (CLGSA) which has a good ability to search and finding optimal solutions. The effectiveness of the proposed model is estimating on Wisconsin breast cancer data set available in the UCI Machine learning source in the University of California, Irvine. We testify the data over the parameters of classification of accurateness, sensitivity as well as specificity with a much better and more responsive 10-fold cross validation method; which is considered as a reliable diagnostics model in the medical field. Experiment results have clearly shown that the proposed approach will turn out to be a calculative and decisive medium for cancer detection in the field of medicine.

A document analysis of how the concept of health is incorporated in care plans when using the nursing diagnosis classification system (NANDA-I) in relation to individuals with bipolar disorder.

Nursing diagnosis classification systems have been developed to help nurses identify problems and patient needs. However, the question of how classification systems have adopted the concept of health has been given little attention. The aim was to explore and analyse which perspectives on health are incorporated into the NANDA-I-based care plans of individuals with bipolar disorder. A document analysis was used to systematically review and analyse care plans based on nursing diagnosis classification system. Thematic analysis was used as an analytic tool. The results are presented in three themes: (i) health as strengths; (ii) health as satisfaction; and (iii) health as behaviour and functionality. We discuss how health is deeply embedded in nursing diagnosis classification system-based care plans. The care plans correspond to the holistic perspective, as they acknowledge the existence of health even during illness. Further, health is viewed as nonstatic and discussed from the perspective of person-centred care.

The healthcare response to human trafficking: A need for globally harmonized ICD codes.

In a Perspective, Jordan Greenbaum and Hanni Stoklosa make the case for inclusion of codes for human trafficking in international diagnosis classification systems.

Nyelvtanulási módszerek és technikák speciális nevelési igényű gyerekek számára

We can read about the problem in several forums and it is one of the most researched neural disorders. In June 2018, the WHO Diagnosis Classification System displayed ICD-11 (The International Statistical Classification of Diseases and Related Health Problems) as a learning disorder that includes reading, writing and counting difficulties. We are not talking about the disorder of intellectual development, but rather as a result of the disruption of perception, movement and sensory integration. This can also occur as a result of genetic but environmentalinduced developmental disorders. In this paper, we will review the difficulties of reading, writing and counting which causes the difficulties of performance and how it occurs in children when learning a foreign language, and how we can help them with the process.

The spine instability neoplastic score (SINS) in the assessment of response to radiotherapy for bone metastases.

Vertebral metastases are often causing pain and spine instability. Radiotherapy is of significant benefit for painful spine metastases but the response can be very variable. The spine instability neoplastic score (SINS) is a recent classification system for diagnosis of spinal instability caused by vertebral metastases. We analysed the degree of pain relief, the need of drug therapy and the imaging features and the SINS before and after radiotherapy. In particular, we investigated the possible correlation of spine instability defined by pre-treatment SINS with pretreatment pain and with response to radiotherapy. This study included 121 patients with spine metastases treated with palliative 3D conformal radiotherapy. Pain "at rest" and "breakthrough pain", need for drug therapy in terms of "anti-inflammatory", "weak opioid", "strong opioid", imaging studies and SINS were assessed before and after radiotherapy. Statistical analysis was performed by the correlation coefficient of Spearman and Kruskal-Wallis test. Pain relief after radiotherapy was observed in 50.4 and 57.8% of patients in terms of pain at rest and breakthrough pain, respectively. The correlation between pain before radiotherapy and SINS was not statistically significant for both pain at rest (p=0.4) and breakthrough pain (p=0.49). The correlation between pain response after radiotherapy and SINS was statistically significant for both pain at rest (p=0.007) and breakthrough pain (p=0.047). The degree of instability, classified according to SINS, resulted to be predictive factor for pain response after radiotherapy. SINS might become a valid tool to identify those patients who can benefit the most from radiotherapy.

Comparison of inter- and intra-observer reliability among the three classification systems for cervical spinal canal stenosis.

The aim is to analyze the agreement between different types of physicians in terms of the inter-observer and intra-observer reliability in addition to the agreement between the experienced and non-experienced physicians with respect to three different classification systems for diagnosis of cervical spinal canal stenosis. Total nine doctors including experienced group of three doctors and non-experienced group of six doctors classified the patients according to three different classification in an independent, blinded manner using magnetic resonance imaging (MRI) to diagnose cervical canal stenosis. MRI slice included sagittal plane (midline cut) and an image slice from each horizontal plane that penetrated the right center of each disk (C3-4, C4-5, C5-6, and C6-7) was made by PPT format. For the inter-observer reliability, Vaccaro et al.'s classification system showed the excellent reproducibility, followed by Muhle et al. and Kang et al. All three classification systems showed excellent reproducibility and substantial agreement in terms of the intra-observer reliability. All three classification systems showed excellent reproducibility and also displayed a substantial agreement. The classification system used by Vaccaro et al. was proven to be a method with substantial agreement both in the experienced group and the non-experienced group. It can be a useful classification system for simplifying communication among all physicians.

Particle swarm optimized computer aided diagnosis system for classification of breast masses

Particle swarm optimized computer aided diagnosis system for classification of breast masses

1793 - The spine instability neoplastic score (SINS) in the assessment of response to radiotherapy for bone metastases

Vertebral metastases are often causing pain and spine instability. Radiotherapy is of significant benefit for painful spine metastases but the response can be very variable. The spine instability neoplastic score (SINS) is a recent classification system for diagnosis of spinal instability caused by vertebral metastases. We analysed the degree of pain relief, the need of drug therapy and the imaging features and the SINS before and after radiotherapy. In particular, we investigated the possible correlation of spine instability defined by pre-treatment SINS with pretreatment pain and with response to radiotherapy. This study included 121 patients with spine metastases treated with palliative 3D conformal radiotherapy. Pain “at rest” and “breakthrough pain”, need for drug therapy in terms of “anti-inflammatory”, “weak opioid”, “strong opioid”, imaging studies and SINS were assessed before and after radiotherapy. Statistical analysis was performed by the correlation coefficient of Spearman and Kruskal–Wallis test. Pain relief after radiotherapy was observed in 50.4 and 57.8% of patients in terms of pain at rest and breakthrough pain, respectively. The correlation between pain before radiotherapy and SINS was not statistically significant for both pain at rest (p = 0.4) and breakthrough pain (p = 0.49). The correlation between pain response after radiotherapy and SINS was statistically significant for both pain at rest (p = 0.007) and breakthrough pain (p = 0.047). The degree of instability, classified according to SINS, resulted to be predictive factor for pain response after radiotherapy. SINS might become a valid tool to identify those patients who can benefit the most from radiotherapy.