Biography writer
Budget: ₹12,500 – ₹37,500 INR
Lung cancer is one of the most common diseases, and it has the highest morbidity and mortality
rates of any cancer worldwide. As reported by global cancer statistics [1], there were 2.1 million new
cases and 1.8 million deaths from lung cancer in 2018. Approximately one in five cases (18.4%) of lung
cancer leads to death. In oncology, medical imaging plays a vital role in reducing the mortality rate.
Imaging is a non-invasive and painless procedure with the least harmful side e ects for patients. It can
provide detailed anatomical information about the disease by generating visual representations of
tumors. Unlike other invasive methods such as surgeries and biopsies which extract and study a small
portion of tumor tissue, imaging can give a more comprehensive view and analysis of the entire tumor
Appl. Sci. 2019, 9, 2329; doi:10.3390/app9112329 www.mdpi.com/journal/applsci
Appl. Sci. 2019, 9, 2329 2 of 26
region. Moreover, it is preferable for clinical routines that require an iterative analysis of the tumor
during treatment [2].
Computed tomography (CT) is a standard imaging modality for lung cancer diagnosis.
The National Lung Screening Trial (NLST) reported that the lung cancer mortality rate of 15–20% could
be reduced by performing low-dose CT screening [3]. CTs display tumors on cross-sectional images
of the body called “slices”. These slices can be stacked together to reconstruct a three-dimensional
structure which makes possible more comprehensive analysis of tumors. However, the manual
interpretation of the CT scans is prohibitively expensive in terms of time and e ort. Also, it is subject to
the skill and clinical practices of the interpreter; hence, the diagnosis results may su er from inter- and
intra-observer variation [4]. Due to these inconveniences, any real-world application is necessary for
automated interpretations of the CT scans. This fact has strongly motivated computer-aided diagnosis
systems (CADs) to become an extensive research area in the field of biomedical engineering. Scientists
have proposed a vast number of CADs for lung cancer diagnosis using modern image processing and
machine learning techniques. Nonetheless, most of these CADs have focused only on the detection
of pulmonary nodules. Nodules can evoke the possibility of lung cancer and appear as round or
oval-shaped opacities on the CT scans [5]. Nonetheless, the detection of nodules does not provide
sucient information about the severity of the disease to make proper treatment decisions. Generally,
half of the patients who undergo CT screening present more than one nodule, but not all of these
nodules are cancerous [6]. Physicians usually use the terms “nodule” and “tumor” interchangeably
when they are uncertain about the severity of the disease. Indeed, identifying the severity of a tumor
nodule is a principal issue for lung cancer diagnosis.
In clinical practice, the severity of lung cancer can be expressed as di erent stages using the Tumor,
Node, and Metastasis (TNM) system [7]. As the name describes, this system assesses the severity
of the disease based on three descriptors. The first descriptor (T) assesses the characteristics of the
primary tumor such as its size, local invasion, and the presence of satellite tumor nodules. The CT
modality performs T-staging well, because information about the tumor can be easily obtained from
the CT scans. The second descriptor (N) assesses the involvement of the regional lymph nodes. On the
CT scans, lymph nodes appear as small opacities with unclear silhouettes; thus, the CT modality
performs weakly for N-staging [8,9].
rates of any cancer worldwide. As reported by global cancer statistics [1], there were 2.1 million new
cases and 1.8 million deaths from lung cancer in 2018. Approximately one in five cases (18.4%) of lung
cancer leads to death. In oncology, medical imaging plays a vital role in reducing the mortality rate.
Imaging is a non-invasive and painless procedure with the least harmful side e ects for patients. It can
provide detailed anatomical information about the disease by generating visual representations of
tumors. Unlike other invasive methods such as surgeries and biopsies which extract and study a small
portion of tumor tissue, imaging can give a more comprehensive view and analysis of the entire tumor
Appl. Sci. 2019, 9, 2329; doi:10.3390/app9112329 www.mdpi.com/journal/applsci
Appl. Sci. 2019, 9, 2329 2 of 26
region. Moreover, it is preferable for clinical routines that require an iterative analysis of the tumor
during treatment [2].
Computed tomography (CT) is a standard imaging modality for lung cancer diagnosis.
The National Lung Screening Trial (NLST) reported that the lung cancer mortality rate of 15–20% could
be reduced by performing low-dose CT screening [3]. CTs display tumors on cross-sectional images
of the body called “slices”. These slices can be stacked together to reconstruct a three-dimensional
structure which makes possible more comprehensive analysis of tumors. However, the manual
interpretation of the CT scans is prohibitively expensive in terms of time and e ort. Also, it is subject to
the skill and clinical practices of the interpreter; hence, the diagnosis results may su er from inter- and
intra-observer variation [4]. Due to these inconveniences, any real-world application is necessary for
automated interpretations of the CT scans. This fact has strongly motivated computer-aided diagnosis
systems (CADs) to become an extensive research area in the field of biomedical engineering. Scientists
have proposed a vast number of CADs for lung cancer diagnosis using modern image processing and
machine learning techniques. Nonetheless, most of these CADs have focused only on the detection
of pulmonary nodules. Nodules can evoke the possibility of lung cancer and appear as round or
oval-shaped opacities on the CT scans [5]. Nonetheless, the detection of nodules does not provide
sucient information about the severity of the disease to make proper treatment decisions. Generally,
half of the patients who undergo CT screening present more than one nodule, but not all of these
nodules are cancerous [6]. Physicians usually use the terms “nodule” and “tumor” interchangeably
when they are uncertain about the severity of the disease. Indeed, identifying the severity of a tumor
nodule is a principal issue for lung cancer diagnosis.
In clinical practice, the severity of lung cancer can be expressed as di erent stages using the Tumor,
Node, and Metastasis (TNM) system [7]. As the name describes, this system assesses the severity
of the disease based on three descriptors. The first descriptor (T) assesses the characteristics of the
primary tumor such as its size, local invasion, and the presence of satellite tumor nodules. The CT
modality performs T-staging well, because information about the tumor can be easily obtained from
the CT scans. The second descriptor (N) assesses the involvement of the regional lymph nodes. On the
CT scans, lymph nodes appear as small opacities with unclear silhouettes; thus, the CT modality
performs weakly for N-staging [8,9].