Pathological Diagnosis and Classification of Lung Cancer in Small Biopsies and Cytology: Strategic Management of Tissue for Molecular Testing

2011 ◽  
Vol 32 (01) ◽  
pp. 022-031 ◽  
Author(s):  
William Travis ◽  
Natasha Rekhtman
Keyword(s):  
Lung Cancer ◽  
Strategic Management ◽  
Pathological Diagnosis ◽  
Molecular Testing ◽  
Diagnosis And Classification

scholarly journals Deep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of Lung Ct Images

2019 ◽  
Vol 9 (1) ◽  
pp. 1808-1819 ◽  
Keyword(s):  
Lung Cancer ◽  
Feature Extraction ◽  
Deep Learning ◽  
Noise Removal ◽  
Ct Images ◽  
Classification Model ◽  
Diagnosis And Classification ◽  
Effective Diagnosis ◽  
Lung Ct

Lung cancer is a serious illness which leads to increased mortality rate globally. The identification of lung cancer at the beginning stage is the probable method of improving the survival rate of the patients. Generally, Computed Tomography (CT) scan is applied for finding the location of the tumor and determines the stage of cancer. Existing works has presented an effective diagnosis classification model for CT lung images. This paper designs an effective diagnosis and classification model for CT lung images. The presented model involves different stages namely pre-processing, segmentation, feature extraction and classification. The initial stage includes an adaptive histogram based equalization (AHE) model for image enhancement and bilateral filtering (BF) model for noise removal. The pre-processed images are fed into the second stage of watershed segmentation model for effectively segment the images. Then, a deep learning based Xception model is applied for prominent feature extraction and the classification takes place by the use of logistic regression (LR) classifier. A comprehensive simulation is carried out to ensure the effective classification of the lung CT images using a benchmark dataset. The outcome implied the outstanding performance of the presented model on the applied test images.

Pathological Diagnosis and Classification of Gastric Epithelial Tumours

2019 ◽  
pp. 53-82
Author(s):  
Rossella Rotondo ◽  
Flavio Rizzolio ◽  
Tiziana Perin ◽  
Massimiliano Berretta ◽  
Fabrizio Zanconati ◽  
...  
Keyword(s):  
Pathological Diagnosis ◽  
Diagnosis And Classification ◽  
Epithelial Tumours

scholarly journals New Pathologic Classification of Lung Cancer: Relevance for Clinical Practice and Clinical Trials

2013 ◽  
Vol 31 (8) ◽  
pp. 992-1001 ◽  
Author(s):  
William D. Travis ◽  
Elisabeth Brambilla ◽  
Gregory J. Riely
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Cell Carcinoma ◽  
Disease Free Survival ◽  
Bronchioloalveolar Carcinoma ◽  
Molecular Testing ◽  
Adenocarcinoma In Situ ◽  
Histologic Subtype ◽  
Pathologic Classification

We summarize significant changes in pathologic classification of lung cancer resulting from the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) lung adenocarcinoma classification. The classification was developed by an international core panel of experts representing IASLC, ATS, and ERS with oncologists/pulmonologists, pathologists, radiologists, molecular biologists, and thoracic surgeons. Because 70% of patients with lung cancer present with advanced stages, a new approach to small biopsies and cytology with specific terminology and criteria focused on the need for distinguishing squamous cell carcinoma from adenocarcinoma and on molecular testing for EGFR mutations and ALK rearrangement. Tumors previously classified as non–small-cell carcinoma, not otherwise specified, because of the lack of clear squamous or adenocarcinoma morphology should be classified further by using a limited immunohistochemical workup to preserve tissue for molecular testing. The terms “bronchioloalveolar carcinoma” and “mixed subtype adenocarcinoma” have been discontinued. For resected adenocarcinomas, new concepts of adenocarcinoma in situ and minimally invasive adenocarcinoma define patients who, if they undergo complete resection, will have 100% disease-free survival. Invasive adenocarcinomas are now classified by predominant pattern after using comprehensive histologic subtyping with lepidic, acinar, papillary, and solid patterns; micropapillary is added as a new histologic subtype with poor prognosis. Former mucinous bronchioloalveolar carcinomas are now called “invasive mucinous adenocarcinoma.” Because the lung cancer field is now rapidly evolving with new advances occurring on a frequent basis, particularly in the molecular arena, this classification provides a much needed standard for pathologic diagnosis not only for patient care but also for clinical trials and TNM classification.

scholarly journals Artificial intelligence–based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients

2020 ◽  
Vol 4 (14) ◽  
pp. 3284-3294
Author(s):  
Korsuk Sirinukunwattana ◽  
Alan Aberdeen ◽  
Helen Theissen ◽  
Nikolaos Sousos ◽  
Bethan Psaila ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Myeloproliferative Neoplasms ◽  
Area Under The Curve ◽  
Response To Treatment ◽  
Molecular Testing ◽  
Automated Identification ◽  
Machine Learning Approach ◽  
Diagnosis And Classification ◽  
Morphological Assessment

Abstract Accurate diagnosis and classification of myeloproliferative neoplasms (MPNs) requires integration of clinical, morphological, and genetic findings. Despite major advances in our understanding of the molecular and genetic basis of MPNs, the morphological assessment of bone marrow trephines (BMT) is critical in differentiating MPN subtypes and their reactive mimics. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative, and poorly reproducible criteria. To improve the morphological assessment of MPNs, we have developed a machine learning approach for the automated identification, quantitative analysis, and abstract representation of megakaryocyte features using reactive/nonneoplastic BMT samples (n = 43) and those from patients with established diagnoses of essential thrombocythemia (n = 45), polycythemia vera (n = 18), or myelofibrosis (n = 25). We describe the application of an automated workflow for the identification and delineation of relevant histological features from routinely prepared BMTs. Subsequent analysis enabled the tissue diagnosis of MPN with a high predictive accuracy (area under the curve = 0.95) and revealed clear evidence of the potential to discriminate between important MPN subtypes. Our method of visually representing abstracted megakaryocyte features in the context of analyzed patient cohorts facilitates the interpretation and monitoring of samples in a manner that is beyond conventional approaches. The automated BMT phenotyping approach described here has significant potential as an adjunct to standard genetic and molecular testing in established or suspected MPN patients, either as part of the routine diagnostic pathway or in the assessment of disease progression/response to treatment.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Segmentation and Classification of Lung Cancer: A Review

2016 ◽  
Vol 16 (2) ◽  
pp. 82-99 ◽  
Author(s):  
Javeria Amin ◽  
Muhammad Sharif ◽  
Mussarat Yasmin
Keyword(s):  
Lung Cancer

Depression

2017 ◽  
Author(s):  
Philip Cowen
Keyword(s):  
International Classification Of Diseases ◽  
Behavioural Therapy ◽  
Historical Background ◽  
Cognitive Behavioural ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Diagnosis And Classification ◽  
Modern Experience ◽  
Symptoms And Signs

This chapter discusses the symptomatology, diagnosis, and classification of depression. It begins with a brief historical background on depression, tracing its origins to the classical term ‘melancholia’ that describes symptoms and signs now associated with modern concepts of the condition. It then considers the phenomenology of the modern experience of depression, its diagnosis in the operational scheme of ICD-10 (International Classification of Diseases, tenth edition), and current classificatory schemes. It looks at the symptoms needed to meet the criteria for ‘depressive episode’ in ICD-10, as well as clinical features of depression with ‘melancholic’ features or ‘somatic depression’ in ICD-10. It also presents an outline of the clinical assessment of an episode of depression before concluding with an overview of issues that need to be taken into account when addressing approaches to treatment, including cognitive behavioural therapy and the administration of antidepressants.

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