A Web- and Cloud- based Service for the Clinical Use of a CAD

(Computer Aided Detection) System

Automated Detection of Lung Nodules in Thoracic CTs (Computed Tomographies)

M. E. Fantacci

1,2

, A. Traverso

3,4

, S. Bagnasco

, C. Bracco

, D. Campanella

, G. Chiara

Lopez Torres

4,7

, A. Manca

, D. Regge

, M. Saletta

, M. Stasi

, S. Vallero

, L. Vassallo

and P. Cerello

Department of Physics, University of Pisa, Largo Pontecorvo 3, Pisa, Italy

INFN, Sezione di Pisa, Pisa, Italy

Department of Applied Science and Technology, Polytechnic University of Turin, Turin, Italy

INFN, Sezione di Torino, Turin, Italy

Medical Physics Unit, Candiolo Cancer Institute - FPO, IRCCS, Candiolo, Italy

Radiology Unit, Candiolo Cancer Institute - FPO, IRCCS, Candiolo, Italy

CEADEN, Havana, Cuba

Keywords: Web Service, Cloud Computing, Computer Aided Detection, Lung Nodules.

Abstract: M5L, a Web-based Computer-Aided Detection (CAD) system to automatically detect lung nodules in

thoracic Computed Tomographies, is based on a multi-thread analysis by independent subsystems and the

combination of their results. The validation on 1043 scans of 3 independent data-sets showed consistency

across data-sets, with a sensitivity of about 80% in the 4-8 range of False Positives per scan, despite varying

acquisition and reconstruction parameters and annotation criteria. To make M5L CAD available to users

without hardware or software new installations and configuration, a Software as a Service (SaaS) approach

was adopted. A web front-end handles the work (image upload, results notification and direct on-line

annotation by radiologists) and the communication with the OpenNebula-based cloud infrastructure, that

allocates virtual computing and storage resources. The exams uploaded through the web interface are

anonymised and analysis is performed in an isolated and independent cloud environment. The average

processing time for case is about 20 minutes and up to 14 cases can be processed in parallel. Preliminary

results on the on-going clinical validation shows that the M5L CAD adds 20% more nodules originally

overlooked by radiologists, allowing a remarkable increase of the overall detection sensitivity.

1 INTRODUCTION

Lung Cancer is one of the main health issues in

developed countries, accounting for about 20% and

28% of cancer-related deaths in Europe (Malvezzi,

2015) and the United States of America (Society,

2015), respectively, with a five-year survival rate of

only 10-17% (Society, 2015). Computed

Tomography (CT) has been shown to be the most

sensitive imaging modality for the detection of small

pulmonary nodules, which constitutes the first

radiological sign of this pathology. Low dose high

resolution CT-based screening trials are regarded as

a promising technique for detecting early-stage lung

cancers (National Lung Screening Trial, 2011;

Moyer, 2014). However, the identification of early

stage pathological Regions of Interest (ROIs) in low-

dose high-resolution CT scans is a difficult and time

consuming task for radiologists, because of the large

number (300/500) of noisy 2D slices to be analysed.

In order to support radiologists, researchers have

developed CAD algorithms to be applied to CT

scans. Several studies (Das, 2006; Brochu, 2007;

Matsumoto, 2008) reported an improvement in the

sensitivity of radiologists when assisted by CAD

systems, which also act as detection rates equalisers

between observers of different level of experience

(Brown, 2005). The most common approach to make

202

Fantacci M., Traverso A., Bagnasco S., Bracco C., Campanella D., Chiara G., Torres E., Manca A., Regge D., Saletta M., Stasi M., Vallero S., Vassallo L. and Cerello P.

A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung Nodules in Thoracic CTs (Computed Tomographies).

DOI: 10.5220/0006245402020209

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 202-209

ISBN: 978-989-758-214-1

CAD algorithms available in the clinical routine is

the deployment of standalone workstations, usually

equipped with a vendor-dependent Graphic User

Interface (GUI), which presents several drawbacks:

the high fixed cost of software licenses, a dedicated

hardware, their rapid obsolescence. Furthermore, the

computational needs of CAD algorithms, depending

on their complexity, often require powerful and

expensive hardware. The diffusion of Cloud

Computing solutions, accessible via secure Web

protocols, solves almost all the previous issues. In

addition, the SaaS approach provides the possibility

of combining several CADs, with demonstrated

benefits to the overall performance (van Ginneken,

2010). This paper presents the M5L on-demand

CAD system: Section 2 summaries the main features

of the algorithms and the results of their validation,

describes the functional blocks of the M5L on-

demand service (the web front-end and the cloud

back-end) and discusses the methodology that was

adopted to perform stress test on the developed

system and the on-going clinical validation; Section

3 discusses the results of the stress test and the

preliminary results of the clinical validation; Section

4 draws some conclusions and analyses possible

further developments.

2 MATERIALS AND METHODS

2.1 The M5L CAD System

M5L is the combination of two independent CAD

sub-systems: the Channeler Ant Model (lungCAM)

and the Voxel-Based Neural Approach (VBNA).

These two algorithms have a common starting point,

which is the parenchymal volume, obtained with a

3D region growing segmentation algorithm, that also

excludes the trachea and separates the two lungs (De

Nunzio, 2011).

2.1.1 The LungCAM CAD

This algorithm is based on the reproduction of the

life-cycle of colonies of virtual ants (Cerello, 2008),

released from an anthill with the capability to move

along the 3D space determined by the lung volume

in the CT. The motion is accompanied by the release

of pheromone traces along the ant path. The CT

voxel intensity is interpreted as the amount of food

available to the ants and is progressively reduced by

the ant feeding. The evolution of the colony is

determined by a set of rules, which define how ants

move, the released amount of pheromone and the

cycles of reproduction and death. The algorithm

ends when all the ants in the colony have died. The

output of this stage is a pheromone map, a collection

of segmented objects, which are classified by means

of a feed-forward artificial neural network with 13

input features, which take into account both

geometrical (as for example radius, sphericity,

skewness, kurtosis) and intensity (as for example

average, standard deviation and entropy inside and

outside the mask, maximum) characteristics of the

segmented objects, as described in detail in (Lopez

Torres, 2015). The lungCAM algorithm detects both

pleural and internal nodules.

2.1.2 The VBNA CAD

This algorithm makes use of two different

procedures to detect nodules inside the lung

parenchyma (CADI) (Li, 2003; Retico, 2008) and

nodules attached to the pleura (CADJP) (Retico,

2009). The former method models nodules as

spheres with a Gaussian profile, where the centre of

the sphere is chosen to be corresponding to a local

maximum in the voxel intensity. The CADJP

method builds normals to the pleura surface and

each voxel gets a score depending on the number of

normals crossing it. Before combining the CADI and

CADJP results, nodule candidates are classified by a

Support Vector Machine (SVM) using more than

100 voxel-based input features for each voxel (the

intensity values of its 3D neighbors, in particular the

5 x 5 x 5 intensity values, and the eigenvalues of the

gradient matrix and of the Hessian matrix). At the

end of the procedure, each ROI is assigned a degree

of suspicion averaging the score of all the voxels

belonging to it (Camarlinghi, 2012).

2.1.3 M5L Results

The M5L results, obtained combining lungCAM and

VBNA have been evaluated in terms of FROC (Free

Response Receiver Operating) curves, as described

in (Lopez Torres, 2015). The measured M5L

sensitivity, evaluated on several input data-sets,

including both the ANODE09 dataset (van

Ginneken, 2010) and the full LIDC/IDRI database

(Armato, 2011), reaches 80% at 4-8 FP/scan, which,

given the size (more than 1000 scans) and

heterogeneity of the data-sets, is satisfactory.

2.2 The M5L on Demand System

In order to make M5L easily available to

radiologists without any additional hardware or

software requirement, a Web-based interface has

A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung

Nodules in Thoracic CTs (Computed Tomographies)

203

been designed and implemented. Furthermore, in

order to efficiently exploit computational resources,

a Cloud-computing facility was setup. The M5L on-

demand system is then composed by the following

modules: the Web front-end, designed to provide

radiologists with the functionality to exchange

imaging studies and compare diagnoses on the same

studies, handles the CT submission, the on-line

insertion of the medical review and the access to

CAD results; the Cloud Computing back-end,

conceived to optimize the access to the available

computing resources (i.e. virtual machines)

according to the user requests, handles the

algorithms execution. A similar concept was also

recently proposed as possible future development

(Goo, 2015).

2.2.1 The Web Front-end

The M5L service is available as a web application

accessible with any browser from desktop and

mobile devices. With proper credentials, users can

access the service and upload thoracic CT studies in

DICOM format to the remote repository; reviewers

(not necessarily belonging to the same institution)

can insert their findings. The notification system is

based on emails, that alert reviewers when a new

case has to be annotated and when the M5L results

are ready. The entire front-end web interface has

been developed using the DRUPAL free and open

source tool, a content management platform

powering millions of websites and applications

(Coombs, 2009). The DRUPAL’s core is modular,

defining a system of hooks and callbacks which are

accessed internally via Application Programming

Interface (API). This design allows the integration of

third-party contributed modules and themes to

extend and/or override DRUPALs default

behaviours without changing its core code.

DRUPAL easily allows the development of custom

modules in PHP code. In terms of security and data

access, DRUPAL offers the possibility to the site

administrator to define group of users, with different

credentials and this allows, according to user

requests, to protect content access. Two different

modules and the associated user profiles have been

developed, which are briefly described in the

following parts. The submission module is

conceived to be used by a technician operating with

a submitter role, who uploads one or more CT

studies to be analysed and selects one or more

radiologists (not necessarily belonging to the same

institutions) who will review the studies. The

module layout is shown in Figure 1. Users can

submit studies sequentially or asynchronously: an

email will inform them when the M5L analysis and

the review by radiologists are completed. Before

being submitted for computation, all cases are

subject to further validation checks (e.g. image

quality controls) that include a re-anonymisation.

The review module is conceived to be used by a

radiologist. After logging in with reviewer role, a

radiologist can insert the medical annotation of

studies assigned for review during the submission

process. The M5L results are available in different

formats, such as DICOM Structured Report, HTML,

XML and PDF. A custom plug-in for the Osirix

medical imaging viewer (Rosset, 2004) has also

been developed to view CAD marks directly on the

CT scan. The radiologist can operate as first or

second reader. When operating as first reader, the

M5L results are unlocked only after the annotation is

completed and validated. The M5L marks can then

be reviewed ad assessed, with the options to include

them in the annotation, as shown in Figure 2, reject

them as false positive findings or label them as non-

relevant nodules.

Figure 1: Module of the Web interface for the CT

submission to the M5L CAD. Setting the various fields it

is possible to select the exam and the radiologist that will

be asked to review it.

Figure 2: Examples of candidate nodules in the M5L CAD

output and the associated evaluation by the radiologist.

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In order to speed-up the overall double reading time,

an automated matching algorithm compares the

CAD marks with annotated ROIs. The radiologist

can then concentrate only on overlooked findings.

When a radiologist operates as second-reader, the

M5L marks are immediately available.

2.2.2 The Cloud Back-end

One of the issues when running CAD algorithms in

parallel and combining their results is that the

required memory and computing power is large and

not precisely predictable. Furthermore, if the tool is

to be used in potentially massive screening

campaigns, which can be concentrated in a given

period of the year, some flexibility is required in the

allocation of computing resources. In the past,

prototype systems for handling the analysis of

medical images in a distributed environment based

on the use of a GRID infrastructure, used in high-

energy physics, were deployed (Bellotti, 2007).

However, the main issues with Grid Computing are

its rigidity, the complexity of the distributed

structure and the manpower required to manage the

system. This solution does not fit well in the case in

the majority of Medical Physics projects, where a

custom environment must be configured according

to the requirements set by the users. Furthermore,

the management of such infrastructure requires

additional man-power, making the solution not cost-

effective for the hospital. Cloud Computing on the

other hand is a model that enables ubiquitous,

convenient, on-demand network access to a shared

pool of configurable and dynamically accessible

computing resources (e.g., networks, servers,

storage, applications, and services) that can be

rapidly provisioned and released with minimal

management effort or service provider interaction

(Mell, 2011). Resources used by the different cloud

applications are handled in completely separated

environments (sandboxes), so it is impossible for a

user to access the data of another or create damage

elsewhere. Even Cloud administrators cannot access

users resources, once created and properly

configured. These features improve the overall

security, privacy and data preservation. A Cloud

Computing infrastructure can provide functionality

to the users at several levels: basic infrastructure of

network, storage and physical or virtual machines

(Infrastructure As A Service or IaaS); computing

platform with programs and services (Platform as a

Service or PaaS); access to software solutions

(Software As A Service or SaaS). One of the key

Cloud Computing features, as highlighted in (Mell,

2011), is the elasticity of resources allocation, i.e.

the capability to scale (up or down) the resources

according to computing power needs. This point is

even more important in case of a research

Community Cloud, where the users have free access

to resources that are shared between many groups

and projects. The M5L CAD on-demand service is

hosted by the INFN Torino Computing Centre,

which is a Tier-2 of Large Hadron Collider

Computing Grid (Turner, 2006). In order to reduce

the manpower to manage the Tier-2, to support an

increasing number of applications and to offer

computational power to small research projects with

specific needs, a Cloud infrastructure has been

deployed (Bagnasco, 2014). The facility is managed

by OpenNebula, a free and open-source Cloud

Management Platform which allows hardware and

virtual infrastructure control and monitoring, adding

the possibility of virtual machine life-cycle

management (Milojicic, 2011). OpenNebula

orchestrates storage, networking, virtualization,

monitoring, and security technologies to deploy

computing services as virtual machines on

distributed resources. The prototype M5L version

makes use of one physical host as web-server and

several Virtual Machines (VMs) as computational

power. The VMs are deployed in a specific sandbox

(IaaS) within an internal private network and a

virtual router with a public address for the external

access and the communication with the web server

(that has another public IP). Presently, M5L is

allowed to deploy up to 18 VMs, with a total of 48

cores and 100 GB persistent ISCSI (Internet Small

Computer System Interface) storage disk, exported

between all VMs via NFS (Network File System)

and used for temporary storage of input CTs and the

M5L output results. An elastic cluster based on

CernVM Online (Buncic, 2010), a service that can

create clusters with a head node and many workers

based on CernVM OS (CERNVM, 2015), was

configured in order to achieve the capability to scale

resources up or down. VMs can be contextualised

using CernVM Online to define the use of resources,

user settings and automatically install and configure

HTCondor and Elastiq. HTCondor is a specialised

work-load management system (batch system) for

compute-intensive jobs (Tannenbaum, 2001): it

accepts the submission of serial or parallel jobs

(which are placed into a queue and run according to

the selected policy), monitors their progress, and

finally informs the user upon completion. Elastiq

(Berzano, 2014) is a lightweight Python daemon that

allows a cluster of Virtual Machines running a batch

system to scale up and down automatically. When a

A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung

Nodules in Thoracic CTs (Computed Tomographies)

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CT is uploaded to the web service, it is copied to the

head node of the cluster and the M5L CAD

execution is controlled by HTCondor. If some CTs

to be examined are waiting in the HTCondor queue

more than a predefined period of time, new workers

are deployed up to the limit of resources. Conversely

if a worker is idle for more than a predefined period

of time, it is de-allocated. In this way when no exam

is uploaded only a small part of the available

resources is locked, freeing the remaining for other

Cloud users. Users are automatically alerted when

the computations are completed, by means of a

notification system based on e-mails. Since the M5L

CAD is the combination of two independent CAD

subsystems, for each exam two different HTCondor

jobs (one for the VBNA and one for the CAM CAD)

run in parallel. When the analysis is completed for

both CADs, the combination is made. HTCondor is

configured to run a maximum of two jobs at the

same time for each worker node, otherwise it would

assign jobs up to the number of cores of the virtual

machine. This configuration keeps some cores free

to use multi-threading (i.e. to spread a process over

several cores) speeding up the analysis.

2.3 Stress Test Configuration

A stress test was set up to test the performance and

the stability of the system under workloads

comparable with daily clinical practice. Exams are

submitted to M5L in two phases, using a custom

python script: first simulating a peak, then by a

progressive submission from several centres with

different rates. For the first test, in order to simulate

the case in which a node sends all the exams of the

day in one bunch, 100 exams have been submitted to

M5L in less than 10 minutes, filling at once all the

available job slots and leaving some exams in the

HTCondor queue. For the second phase, three

medical centres were configured with different

parameters: a large centre submitting an exam every

10 minutes for a total of 100 exams; a medium

centre submitting an exam every 20 minutes for a

total of 30 exams; a small centre submitting an exam

every 30 minutes for a total of 10 exams. The

numbers of exams for each centre is comparable

with the actual activity of a radiology department in

a hospital of large, medium or small size.

2.4 Clinical Validation

In the literature there are some works aiming at

evaluating the impact of a CAD system on clinical

data (Beyer, 2007; Mang, 2007). These works

confirm the positive contribution of CAD as second

reader. However all the results were obtained using

retrospective studies on relative small databases. In

(Beyer, 2007), for example, the author claims that

possible memory effects could not be neglected. In

addition, the majority of the studies have evaluated

CADs using datasets coming from screening

campaigns. Conversely, lung cancer screening

campaigns do not represent the primary source of

chest CT scans acquired in a hospital. In fact,

clinical studies usually investigate the appearance of

a pulmonary nodule as first sign of an extra-thoracic

metastatic tumour. This diagnostic analysis is

usually performed in oncological patients with an

extra-thoracic cancer. In fact, the early detection of

pulmonary metastases (combined with an adequate

follow-up) can really improve the survival of the

patients (Beattie, 1975). We set up a prospective

observer study aiming at investigating the impact of

the CAD as second-reader in the detection of

pulmonary metastases. The second aim of the study

was to clinically validate the M5L on-demand

system, since we believe that only a direct usage in

clinical practice could give us the possibility to test

all the functionalities of the developed system, and

build new ones if requested by clinicians. Finally,

this observer study has the appeal to collect a dataset

of annotated clinical data to be used for further

clinical investigations. We set up a collaboration

with the Radiology Department of the IRCCS in

Candiolo, Italy. This study was a single-centre cross

sectional study. Each participant underwent chest

CT clinical examinations. Local institution board

approval was obtained to publish the data. Two

different kinds of examinations were used for the

patients in our study: CT with or without contrast

enhancement. Two experienced radiologists (20-35

years of experience) and one young radiologist

(training as resident radiologist, 2 years of

experience) took part in our study. All the

radiologists work in the Radiology Department of

IRCCS Candiolo. Each exam is submitted to the

M5L front-end as soon as acquired, anonymized and

stored in the hospital PACS. To make the procedure

faster we allowed the submission of bunches of 10

cases in each upload. As soon as submitted, the three

radiologists receive an e-mail with the direct link to

annotate the case. Consistent with the second-reader

protocol CAD results become available to

radiologists only when the first unassisted reading

has been completed and validated. Then, the

radiologists receive the link to access and review

CAD results. To speed up the second reading, CAD

marks are automatically compared by our system to

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the pathological ROIs in each annotation. A

matching algorithm associates two findings when

the 3D Euclidean distance between them is smaller

than the mean diameter. In this case, the CAD

finding takes the same properties (e.g. malignancy

score) of the corresponding annotated finding. Using

again the web-form, the radiologist can mark a

finding as: False Positive, Irrelevant or True

Positive. In the irrelevant findings are included the

definition in (van Ginneken, 2010) and all the

nodules smaller than 3 mm in diameters. Building a

robust reference standard is fundamental when

evaluating CAD performances (Armato, 2009). We

defined a reference standard composed by all the

nodules with a diameter larger than 3 mm and with a

malignancy score equal to or larger than 2

(Indeterminate). The malignancy score, marked by

the radiologist, is defined according to (Armato,

2011): subjective assessment of the likelihood of

malignancy, assuming that the scan originated from

a 60-year-old male smoker. The score goes from 0

(Highly Unlikely to be malignant) to 4 (Highly

suspicious to be malignant). To build a reference

standard, all the nodules have been evaluated again

by a pool of two resident radiologists with about 10

years of experience, using the Osirix plugin (Section

2.2.1). They, once received the list of findings, could

select a finding and scroll the images of the

corresponding study. Additional measurement tools

were available to assess and verify the findings size.

The reading protocol was forced consensus.

3 RESULTS

3.1 Stress Test Results

After the stress test completion, a total of 240 exams

were successfully analysed by M5L. On average,

each exam, with about 280 slices, was processed in

about 19 minutes, corresponding to the

computational time of the slower algorithm (usually

lungCAM) plus the time required for the

combination, which is negligible. Figures 3 and 4

show a plot of the number of submitted, running and

completed jobs during the first and second phase of

the stress test, respectively. In the first part, the M5L

CAD processed the submission of a bunch of 100

exams in about 4 hours, while during the second part

the completion rate closely matched the submission

rate. The gap between the start of the submissions

and the peak in the number of jobs being processed,

observed in both figures, corresponds to the waiting

time for the re-allocation of computational resources

temporarily being used by other applications. Since

the M5L CAD cluster is hosted, together with other

applications, by a research infrastructure which is

elastic in the management of resources, it may

happen that all the resources are already allocated to

other applications at the beginning of the

submission. In these cases, the resources are

progressively released and used to deploy M5L

worker nodes. In this transition period the number of

analysed exams is limited, but as soon as the full

M5L computational power is accessed the

completion rate is constant and consistent. Even so,

in both cases the system reacted to the workload as

expected and the results are fully in line with the

needs for a practical clinical use of the M5L CAD.

More intensive testing with a greater number of

cases is planned, so as to estimate the required

infrastructure size as function of the load.

Figure 3: Distribution of jobs and completion time during

the first phase of the stress-test: 100 exams submitted at

once caption has more than one line so it has to be set to

justify.

Figure 4: Distribution of jobs and completion time during

the second phase of the stress-test: 140 exams with a slow

and steady submission rate.

3.2 Clinical Validation Preliminary

Results

The reference standard can be divided, according to

the level of agreement, in nodules annotated by at

least 1 out of 3 radiologists;

nodules annotated by at

least 2 out of 3 radiologists;

nodules annotated by 3

out of 3 radiologists.

For each category it is possible

to compute the standalone

CAD sensitivity, which is

defined as Sensitivity =

TP/(TP+FN),

where FN is

the number of False Negatives (i.e. the

nodules

missed by the CAD) and TP are the nodules

of the

CAD matched with an annotated nodule.

This

quantity measures the performances of our system

with respect to the reference standard. Table 1

refers

to the preliminary results obtained on 70 scans. The

A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung

Nodules in Thoracic CTs (Computed Tomographies)

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first row is the number of nodules inserted in the

reference standard. The first three columns

correspond

respectively to the annotations of each

radiologist. The last column refers to the list of the

nodules obtained when considering the nodules in

the reference standard

annotated by at least three

radiologists. The second row is the number of false

positives computed as the fraction between all the

CAD findings marked

as False Positives and the

total number of scans (70). The third row is the

number of CAD False Negatives (FN).

Table 1: Preliminary results on 70 scans. The first three

columns refer to the nodules in the reference standard

annotated by each of the radiologists. The last column

refers to all the nodules in the reference standard

annotated by at least three RADs.

R0 R1 R2 R0 & R1 &

Annotated

nodules

35 38 21 17

FP/scan 3.25 4.02 3.4 3.6

FN CAD 4 6 2 2

CAD sens 90% 86% 90% 89%

CAD findings marked as True Positive and not

originally included in the unassisted annotations

represent nodules overlooked by the radiologists and

added by the CAD. They should then be included in

the reference standard, which is finally formed by all

the nodules annotated by the radiologist, plus the

true positives added by the CAD. Table 2 shows the

preliminary results on 70 cases. The third column is

the number of nodules overlooked by each

radiologist, together with the relative percentage

increase. The last column is the sensitivity of the

radiologist plus our CAD, considered together as an

only reader.

4 CONCLUSIONS

The M5L CAD was developed for the automated

detection of pulmonary nodule in chest CT scans; its

performance has been already extensively validated

on 1043 CT scans (Lopez Torres, 2015). In order to

simplify the access to the M5L CAD (or any other,

in principle) across a widespread users community, a

dedicated infrastructure based on a Web front-end

interface and a Cloud Computing back-end has been

designed, implemented and operated. On average

each exam can be processed in about 19 minutes.

The proposed approach solves the issue of making

CAD functionality available to users without

requiring any software installation or dedicated

hardware and, if properly scaled, provides the

necessary amount of computational power for a

large scale service. The system allows to use the

CAD both as concurrent reader and second-reader

mode. In this last scenario, the second reading time

is sensitively decreased with an automated matching

algorithm comparing annotated nodules with CAD

marks: this automated procedure allows radiologists

to save time and focus on overlooked findings.

Preliminary results on the ongoing clinical

validation show that the M5L CAD adds about a

20% of the nodules originally overlooked by the

radiologists, producing a remarkable increase of the

overall (RAD+CAD) sensitivity. We are planning to

finalize these results on a database of 220 scans and

to perform additional large scale stress tests,

together with expanding the network of our users.

Table 2: Results of the second reading on 70 scans. The

first three columns refer to the nodules in the reference

standard annotated by each radiologist, plus CAD TPs

originally overlooked by the radiologists.

R0 &

CAD

R1 &

CAD

R2 &

CAD

R0 &

R1 &

R2 &

CAD

Annotated

nodules

38 41 40 36

FP/scan 3.25 4.02 3.4 3.6

FN CAD 4 6 2 2

TP added

by CAD

(incr %)

(+9%)

(+8%)

(+90%)

(+93%)

Sensitivity 91% 87% 95% 95%

ACKNOWLEDGEMENTS

The authors thank the technical staff of the INFN

Computer Centre in Torino, for their contribution in

keeping the infrastructure functional at all times.

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A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung

Nodules in Thoracic CTs (Computed Tomographies)

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