Electronic Health Record (EHR) system enables clinical decision support. In this study, a set of 112 abdominal computed tomography imaging examination reports, consisting of 59 cases of hepatocellular carcinoma (HCC) or liver metastases (so-called HCC group for simplicity) and 53 cases with no abnormality detected (NAD group), were collected from four hospitals in Hong Kong. We extracted terms related to liver cancer from the reports and mapped them to ontological features using Systematized Nomenclature of Medicine (SNOMED) Clinical Terms (CT). The primary predictor panel was formed by these ontological features. Association levels between every two features in the HCC and NAD groups were quantified using Pearson's correlation coefficient. The HCC group reveals a distinct association pattern that signifies liver cancer and provides clinical decision support for suspected cases, motivating the inclusion of new features to form the augmented predictor panel. Logistic regression analysis with stepwise forward procedure was applied to the primary and augmented predictor sets, respectively. The obtained model with the new features attained 84.7% sensitivity and 88.4% overall accuracy in distinguishing HCC from NAD cases, which were significantly improved when compared with that without the new features.
BackgroundOwing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited.MethodsPre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People’s Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO).ResultsThe Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar’s test p = 0.0003).ConclusionsA Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients.
Background: Vestibular schwannoma is an intracranial tumor which can lead to devastating neurological deficit and is prone to recurrence after surgery. Patients with inherited neurofibromatosis type 2 (NF2) syndrome are particularly susceptible to bilateral and aggressive schwannomas. However, the genome of vestibular schwannomas is not well known. There is an imminent need of developing effective chemotherapeutic agents either as a primary treatment modality or as adjuvant therapy for these patients. Methods: Here, we subjected both sporadic and NF2-related schwannomas to high-throughput DNA sequencing using a panel of therapeutically important cancer-related genes, in order to determine if targetable genetic changes are present in schwannomas. Results: A number of variants were detected in the genes NRAS, PDGFRA, KIT, and EGFR, in both sporadic and
The rapid emergence of molecular diagnostic platforms has revolutionized the diagnostic approaches in hematology laboratory. Fluorescence in-situ hybridization, polymerase chain reaction and DNA sequencing are common techniques used in routine clinical laboratories for the diagnosis of hematological diseases. Different molecular techniques are indicated in different situations. This paper describes the utility of common molecular techniques. Fluorescence in-situ hybridization is specific for detection of chromosomal abnormalities using fluorescent labeled targeting probe. Polymerase chain reaction amplifies target DNA and reverse transcription polymerase chain reaction amplifies target RNA for the analysis of gene and its expression level. Real-time polymerase chain reaction is highly sensitive for detection of minimal residual disease in hemic malignancies. Gap-polymerase chain reaction is often employed for diagnosis of large deletions such as in alpha thalassemia. Allele-specific polymerase chain reaction is commonly used for single nucleotide polymorphism detection which is common in beta thalassemia, myeloproliferative neoplasm and acute leukemia. Inverse shifting-polymerase chain reaction can be employed for the detection of large genetic rearrangements such as those seen in hemophilia A. For genetically complex diseases such as hemophilia A, which involves a great variety of mutations in large genes, high resolution melting analysis can be used to scan for point mutations. Any suspected mutations are confirmed using post-PCR technologies, such as DNA sequencing. Although conventional diagnostic methods are able to provide a basic analysis in most cases, molecular technologies generate valuable genetic information that can refine diagnosis, better predict prognosis and facilitate disease monitoring. Fluorescence in situ hybridization (FISH)FISH was developed in early 80s and became one of the most sensitive assays for localization of specific nucleic acid sequences and detection of numerical chromosome abnormalities, structural chromosomal rearrangements and cryptic abnormalities [5]. Bone marrow aspirate and peripheral blood smears can be used for FISH analysis [2,4]. FISH makes use of fluorescent probes which hybridize only to complementary sequences and the resulting signals are examined under a fluorescent microscope. The utility of specific FISH J o ur nal o f M e d ic al Dia g n o s ti c Method s
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