Fully Automated Pipeline for the Detection and Segmentation of Non-Small Cell Lung Cancer (NSCLC) on CT Images
Fully Automated Pipeline for the Detection and Segmentation of Non-Small Cell Lung Cancer (NSCLC) on CT Images
Accurate segmentation of lung tumor is essential for treatment planning, as well as for
 monitoring response to therapy. It is well-known that segmentation of the lung tumour by
 different radiologists gives different results (inter-observer variance). Moreover, if the
 same radiologist is asked to repeat the segmentation after several weeks, these two
 segmentations are not identical (intra-observer variance). In this study we aim to develop an
 automated pipeline that can produce swift, accurate and reproducible lung tumor
 segmentations.
In this study, we aim to develop and test an automated deep learning detection and
 segmentation software for non-small cell lung cancer (NSCLC) that can automatically detect
 and segment tumors on CT scans and thus reduce the human variation. We will assess the level
 of agreement between a group of radiologists, performing manual versus semi-automatic tumour
 segmentation. To do so, we will provide radiologists with two sets of CT scans. The first set
 will be segmented manually; the second one will be segmented using the automated software
 program.

 Subsequently, we will use the inter- and intra-observer variance from the clinical study in a
 simulation or modeling study. We also compare the time needed and the consistency in
 segmentations by the software to medical doctors performance.

 Reliability and Agreement study:

 Primary tumours of 25 lung cancer patients will be delineated by 6 segmentation experts.

 1. Assess agreement between automatic segmentation and radiologists' segmentation The
 primary tumours of 25 patients will be manually segmented by the radiologists and
 automatically by the the tool. The time needed to perform this task and the
 reproducibility of the segmentation will be recorded. The degree of overlap between the
 ROs and the automatic contour will be assessed pairwise using the Dice coefficient.

 2. Delination of tumours by the experts, assisted by the software tool For another 25
 patients, the experts will be provided with an automatic delineation, performed by the
 tool. They have the possibility to adjust and validate it. The time needed will be
 recorded. The difference between the mean overlap fraction in the first situation
 (manual delineation of experts) and the second situation (delineation of experts+
 software tool) will be assessed, using a multi-observer Dice coefficient.

 3. Assessment of intra-observer variance The experts will repeat the segmentation of the
 lung tumours after 2 weeks. They will repeat the manual segmentation (n=25) and the
 semi-automatic segmentation (n=25). This will make it possible to assess the
 intra-observer variance in both situations.

 4. Qualitative assessment of the experts' preferences using an in-house developed
 visualization toolbox.
Inclusion Criteria:

 -  Availability of CT scans

 -  Availability of definite diagnosis

 Exclusion Criteria:

 -  Lack of segmentations