Overlooking cancer: Practising cancer diagnostics in the subjunctive mood

2018 ◽  
Vol 14 (27) ◽  
Author(s):  
Michal Frumer
Keyword(s):  
Lung Cancer ◽  
Outpatient Clinic ◽  
Lung Nodule ◽  
Cancer Diagnostics ◽  
Ct Scans ◽  
Lung Nodules ◽  
Risk And Uncertainty ◽  
Ethnographic Fieldwork ◽  
Subjunctive Mood

The paper explores modes of uncertainty, when watching the possible development of signs of lung cancer at a lung cancer outpatient clinic. Based on ethnographic fieldwork at a clinic in Denmark, it is presented how potential signs of lung cancer, termed nodules, on people’s lungs call to be managed due to the hope and aspirations of alleviating cancer. The paper suggests that the significance of the uncertainties of lung nodules is tried out by watching the nodule with follow-up CT scans and opposed by focusing on intervention. Approaching the management of uncertainties as in a subjunctive mood in addition to a focus on cautionary but qualified guessing, the paper proposes that the physicians try out a possible but indeterminate future of cancer, to contain the prognostic and existential uncertainties by acting ‘as if’ cancer will develop. However, in this cautionary managing of cancer doubt and uncertainty, ambiguities are (re-)produced, leaving an interim certainty: This lung nodule is most likely not and may never become cancer. In this sense, the paper argues that how we as humans are practising the management of risk and uncertainty is shared across different, specific locations.

Overlooking cancer: Practising cancer diagnostics in the subjunctive mood

2018 ◽  
Vol 14 (27) ◽  
Author(s):  
Michal Frumer
Keyword(s):  
Lung Cancer ◽  
Outpatient Clinic ◽  
Lung Nodule ◽  
Cancer Diagnostics ◽  
Ct Scans ◽  
Lung Nodules ◽  
Ethnographic Fieldwork ◽  
Subjunctive Mood ◽  
Management Of Uncertainty

The paper explores the management of uncertainty, when watching the possible development of signs of lung cancer at a lung cancer outpatient clinic. Based on ethnographic fieldwork at a clinic in Denmark, it is presented how potential signs of lung cancer, termed nodules, on people’s lungs call to be managed due to the hope and aspirations of alleviating cancer and cancer related suffering. The paper suggests that the significance of the uncertainties of lung nodules is tried out by watching the nodule with follow-up CT-scans and opposed by focusing on intervention. Approaching the management of uncertainties as in a subjunctive mood, the paper proposes that the physicians try out a possible but indeterminate future of cancer, to contain the prognostic horizon and uncertainties by acting ‘as if’ cancer will develop. However, in this cautionary managing of cancer doubt and uncertainty, ambiguities are (re-)produced, leaving an interim certainty: This lung nodule is most likely not and may never become cancer.

scholarly journals Development and outcomes of a comprehensive multidisciplinary incidental lung nodule and lung cancer screening program

2020 ◽  
Author(s):  
Gregory LeMense ◽  
Ernest A. Waller ◽  
Cheryl Campbell ◽  
Tyler Bowen
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Screening Program ◽  
Lung Nodule ◽  
Lung Cancer Screening ◽  
Treatment Modalities ◽  
Lung Nodules ◽  
Hospital System ◽  
Number Of Patients

Abstract Background: Appropriate management of lung nodules detected incidentally or through lung cancer screening can increase the rate of early-stage diagnoses and potentially improve treatment outcomes. However, the implementation and management of comprehensive lung nodule programs is challenging. Methods: This single-center, retrospective report describes the development and outcomes of a comprehensive lung nodule program at a community practice in Tennessee. Computed tomography (CT) scans potentially revealing incidental lung nodules were identified by a computerized search. Incidental or screening-identified lung nodules that were enlarging or not seen in prior scans were entered into a nodule database and guideline-based review determined whether to conduct a diagnostic intervention or radiologic follow-up. Referral rates, diagnosis methods, stage distribution, treatment modalities, and days to treatment are reported. Results: The number of patients with lung nodules referred to the program increased over 2 years, from 665 patients in Year 1 to 745 patients in Year 2. Most nodules were incidental (62%-65%). Nodules identified with symptoms (15.2% in Year 1) or through screening (12.6% in Year 1) were less common. In Year 1, 27% (182/665) of nodules required a diagnostic intervention and 18% (121/665) were malignant. Most diagnostic interventions were image-guided bronchoscopy (88%) or percutaneous biopsy (9%). The proportion of Stage I-II cancer diagnoses increased from 23% prior to program implementation to 36% in Year 1 and 38% in Year 2. In screening cases, 71% of patients completed follow-up scans within 18 months. Only 2% of Year 1 patients under watchful waiting required a diagnostic intervention, of which 1% received a cancer diagnosis. Conclusions: The current study reports outcomes over the first two years of a lung cancer screening and incidental nodule program. The results show that the program was successful, given the appropriate level of data management and oversight. Comprehensive lung nodule programs have the potential to benefit the patient, physician, and hospital system.

THE APPLICATION OF ACR LUNG-RADS AND LUNG LOW DOSE COMPUTED TOMOGRAPHY IN DIAGNOSIS AND FOLLOW UP LUNG NODULES: EARLY REPORT IN 6 CASES

2017 ◽  
pp. 271-280
Author(s):  
Thi Ngoc Ha Hoang ◽  
Trong khoan Le
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Low Dose ◽  
Early Report ◽  
False Negative ◽  
Lung Nodule ◽  
University Hospital ◽  
Lung Nodules ◽  
Low Dose Computed Tomography

Background: The lung low dose computed tomography and ACR LungRADS was routinely apply in diagnosis and follow up lung nodules in Hue University Hospital. A pulmonary nodule is defined as a rounded or irregular opacity, well or poorly defined, measuring up to 3 cm in diameter. Early detection the malignancy of nodules has a significant role in decreasing the mortality, increasing the survival time and consider as early diagnosis lung cancer. Classification of American College of Radiology, LungRADS, is a newly application but showed many advantages in comparison with others classification such as increasing positive predict value (PPV), no result of false negative and cost effectiveness. These 6 case report in order to show an early evaluation of the application of ACR LungRADS in diagnosis and follow up lung nodules at Hue University Hospital. Key words: LungRADS, screening lung nodule, low dose CT, lung cancer

Nodule net: A prospective safety net program to reduce loss to follow-up and increase early detection of lung cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1564-1564
Author(s):  
Harpreet Singh ◽  
Chinmay Jani ◽  
Arashdeep Rupal ◽  
Arti Tewari ◽  
Alexander Walker ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Primary Care ◽  
Program Effectiveness ◽  
Lung Nodule ◽  
Safety Net ◽  
Management Program ◽  
Lung Nodules ◽  
Comparison Results ◽  
Nurse Navigator

1564 Background: Inadequate follow-up of suspicious lung nodules can result in a delay in diagnosis and potential progression to advanced staged lung cancer. A multidisciplinary lung nodule program entitled "Nodule Net" was implemented in 2017 to provide a safety net, increase the rate of follow-up, streamline management. The program consisted of a multidisciplinary team with EMR notification by the radiologist to a centralized nurse navigator for inclusion in a follow-up database, outreach with reminders to the primary care provider if follow-up was not completed, and referral for management where appropriate. In this study, we sought to evaluate program effectiveness in tracking and rate of follow-up imaging of suspicious pulmonary nodules. Methods: 2,398 chest CT scans were reviewed between January and May 2018 for the presence of a lung nodule that required follow-up. Nodules known to be inflammatory or associated with a metastatic malignancy were excluded. Baseline demographics, medical history, primary care affiliation, type of imaging scan, nodule characteristics, and presence and specifics of follow-up recommendations were collected. For reports that did not include a follow-up recommendation, Fleischner’s recommendations were applied or an independent pulmonologist’s review was completed. The rate of follow-up imaging was recorded and compared with historical rates prior to Nodule Net implementation. Prevalence ratios were generated for each comparison. Results: 1,367 (57%) reported lung nodules. Recommendations for follow-up imaging were recorded in 632 (46.2%), and 523 (82.8%) of these were reported to the program navigator. The rate of follow-up completion of those referred to the program was significantly higher [408 (78%)] than standard of care prior to program implementation [442/1202 (36.8%), (2.90, 95% CI: 2.65-3.18)]. Out of 408 patients who completed follow-up, nodule net outreach was required in 116 (28.4%). Of these 116, malignancy was identified in 4/116 (3.4%). Increased nodule size requiring referral was identified in 17 (14.7%). Out of 109 who were not transmitted to the program navigator and not present in the database, 57 (52.3%) had completed the recommended follow-up compared with 78% among those referred (1.49, 95% CI:1.23-1.79). Conclusions: Management of lung nodules is a complex process with poor follow-up completion reported in prior studies (29%-33%). Implementation of a multidisciplinary lung nodule care program for tracking lung nodules led to a significant increase in completion of recommended follow-up imaging. Developing a comprehensive lung nodule management program using software and navigation may further enhance detection, reduce human errors, augment the necessary follow-up for suspicious lung nodules, and ultimately the prevalence of advanced stage lung cancer.

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

scholarly journals Application of Deep Learning in Lung Cancer Imaging Diagnosis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Wenfa Jiang ◽  
Ganhua Zeng ◽  
Shuo Wang ◽  
Xiaofeng Wu ◽  
Chenyang Xu
Keyword(s):  
Lung Cancer ◽  
Computer Vision ◽  
Deep Learning ◽  
Malignant Tumors ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodules ◽  
X Rays ◽  
Imaging Diagnosis ◽  
Risk Of Cancer

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

scholarly journals AUTOMATED DETECTION SYSTEM FOR LUNG MASSES AS A STAGE OF ARTIFICIAL INTELLIGENCE DEVELOPMENT IN THE DIAGNOSTICS OF LUNG CANCER

2018 ◽  
pp. 62-68 ◽  
Author(s):  
B. M. Moiseenko ◽  
A. A. Meldo ◽  
L. V. Utkin ◽  
I. Yu. Prokhorov ◽  
M. A. Ryabinin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Industrial Revolution ◽  
Detection System ◽  
Lung Nodule ◽  
Cancer Diagnostics ◽  
Digital Information ◽  
Intellectual Activity ◽  
Domestic Literature ◽  
Modern Computer

In the century of the fourth industrial revolution, there is a rapid progress of technological developments in medicine. Possibilities of collecting large amounts of digital information and the modern computer capacity growth are reasons for the increased attention to artificial intelligence (AI) and its role in the diagnostics and the prediction of diseases. In the diagnostics, AI aims to model the human intellectual activity, providing assistance to a practicing doctor in the processing of big data. Development of AI can be considered as a way for implementation and ensuring of national political and economic interests in the health care improvement. Lung cancer is on the first position of cancer incidences. This implies that the development and implementation of computed-aided systems for lung cancer diagnostic is very urgent and important. The article presents the results concerning the development of a computed-aided system for the lung nodule detection, which is based on the processing of computed tomography data. Perspectives of the AI application to the lung cancer diagnostics are discussed. There is a few information about a role of Russian developments in this area in foreign and domestic literature.

M23 Follow-up in surgically treated lung cancer: CT scans versus chest radiographs

2018 ◽  
Author(s):  
C Rowan ◽  
H Lee Evans ◽  
K McCarthy-Bell ◽  
P Blaxill ◽  
A Ameri
Keyword(s):  
Lung Cancer ◽  
Chest Radiographs ◽  
Ct Scans

scholarly journals 1.2 Cm Lung Nodule Developed into Metastatic Lung Adenocarcinoma in Two Years in Patient Low Socioeconomic Status

2020 ◽  
Vol 2 (2) ◽  
pp. 1-2
Author(s):  
Mukesh Kumar ◽  
◽  
Fnu Sonia ◽  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Socioeconomic Status ◽  
Lung Adenocarcinoma ◽  
Low Socioeconomic Status ◽  
Lung Nodule ◽  
Stage Iv ◽  
Metastatic Lung ◽  
Size Growth

Lung cancer is number one cause of cancer mortality in United States both in men and women. Lung cancer is uncommon in patients younger than 35 years with no smoking and family history. Malignancy from lung nodule depends on size, growth rate, borders, calcification and location. Appropriate follow up for lung nodules in older patient with risk factors has been well described in literature based on various researches. However there is very limited data regarding follow up and management of lung nodule in younger patient with risk factors. We describe a patient who was 30 year old when he presented with acute appendicitis and incidentally found to have lung nodule of 1.2 cm. It was decided that patient should follow up as an outpatient for lung nodule. As patient was uninsured with poor socioeconomic he never followed up as outpatient. After 2 years patient was diagnosed with stage IV lung adenocarcinoma and died shortly after. Guidelines should be used in the proper clinical context as a tool to help with patient management, though exceptions always exist. Some expert believe lung nodule between 8-30 mm in patient with poor follow-up due to socioeconomic status, psychological issues, or young age should get complete resection of nodule.

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