A Prostate Cancer Computer Aided Diagnosis Software including

Malignancy Tumor Probabilistic Classiﬁcation

Alessandro Savino

1,2

, Alfredo Benso

1,2

, Stefano Di Carlo

, Valentina Giannini

, Anna Vignati

Simone Mazzetti

, Gianfranco Politano

and Daniele Regge

Department of Control and Computer Engineering, Torino, Italy

Consorzio Interuniversitario Nazionale per l’Informatica, Verres (AO), Italy

Institute for Cancer Research and Treatment, Candiolo (TO), Italy

Keywords:

Prostate Cancer, Computer Aided Diagnosis, Malignancies Probabilistic Classiﬁcation, Magnetic Resonance

Imaging (MRI), Software Design.

Abstract:

Prostate Cancer (PCa) is the most common solid neoplasm in males and a major cause of cancer-related death.

Screening based on Prostate Speciﬁc Antigen (PSA) reduces the rate of death by 31%, but it is associated

with a high risk of over-diagnosis and over-treatment. Prostate Magnetic Resonance Imaging (MRI) has the

potential to improve the speciﬁcity of PSA-based screening scenarios as a non-invasive detection tool. Re-

search community effort focused on classiﬁcation techniques based on MRI in order to produce a malignancy

likelihood map. The paper describes the prototyping design, the implemented work-ﬂow and the software

architecture of a Computer Aided Diagnosis (CAD) software which aims at providing a comprehensive di-

agnostic tool, including an integrated classiﬁcation stack, from a preliminary registration of images to the

classiﬁcation process. This software can improve the diagnostic accuracy of the radiologist, reduce reader

variability and speed up the whole diagnostic work-up.

1 INTRODUCTION

Prostate cancer (PCa) is the most common malig-

nancy affecting men in the world. It represents the

third cause of cancer death in industrialized coun-

tries (Ferlay et al., 2013). Due to the widespread use

of screening tests, such as Prostate Speciﬁc Antigen

(PSA), ﬁve-year survival rate is high. Still, PCa di-

agnosis is obtained by transrectal ultrasound (TRUS)

guided core biopsy: several samples, usually from

12 to 16, are taken from different parts of the gland

in order to increase the probability of detecting can-

cer. Currently, there are several limitations to the ap-

plied diagnostic-therapeutic work-ﬂow, which nega-

tively affect the quality of life of the subjects and/or

may lead to over or under-treatment of PCa (Lujan

et al., 2004). Limitations can be summarized as fol-

lows:

• PSA test has a low speciﬁcity in detecting PCa,

and can cause potential harms: additional medical

visits, adverse effects of prostate biopsies, anxi-

ety, and over-diagnosis leading to over-treatment

with its associated side effects (bowel urgency,

urinary leakage, erectile dysfunction).

• Whenever PCa is suspected, patients undergo

biopsy guided by TRUS, having a low detection

rate and low-speciﬁcity. This may determine a

diagnostic delay, which leads to a higher rate of

recurrence and to a lower patient survival rate.

• Because of the lack of techniques for precise PCa

localization and staging, current treatment strate-

gies involve the whole gland. Due to the inherent

risks associated with surgical resection and radio-

therapy, many patients developsevere side effects.

Magnetic Resonance Imaging (MRI) has been

shown promising in the identiﬁcation of PCa. Physi-

cians commonly exploit it for preoperativeevaluation,

cancer staging, and image guidance for prostate inter-

ventions (T¨urkbey et al., 2012).

In particular, by combining different MRI se-

quences, it is possible to derive anatomical and func-

tional information useful to provide not only tumor

localization but also to distinguish low risk tumors

(amenable to a wait-and-see strategy or to active

Savino A., Benso A., Di Carlo S., Giannini V., Vignati A., Politano G., Mazzetti S. and Regge D..

A Prostate Cancer Computer Aided Diagnosis Software including Malignancy Tumor Probabilistic Classiﬁcation.

DOI: 10.5220/0004799100490054

In Proceedings of the International Conference on Bioimaging (BIOIMAGING-2014), pages 49-54

ISBN: 978-989-758-014-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

surveillance) from high-risk patients that will require

prostatectomy. In those terms, the combining process,

the so called multi-parametric (mp) MRI, brings de-

tection rates of clinically signiﬁcant lesions consis-

tently up to 90% (Liu et al., 2009; Hoeks et al., 2011).

However, the more variables are introduced the more

difﬁcult it is, even for the experienced reader, to in-

tegrate the available information into one reliable ﬁ-

nal report. Usually, the interpretation of a prostate

MRI examination requires a dedicated radiologist. It

is a time consuming task, which still can lead to loose

important information due to the complexity of com-

paring high resolution anatomical features of the T2-

weighted (T2-w) images, with functional data origi-

nating from dynamic contrast enhanced (DCE) stud-

ies and diffusion weighted (DW) imaging. The com-

plexity has been observed in several studies as high

inter-reader variability (Dickinson et al., 2011).

Computer Aided Diagnosis (CAD) softwares can

help the radiologist in the diagnostic work-up and

speed up reporting tasks by automating the mp MRI

process from the different MRI sequences (Padhani

et al., 2011; Hegde et al., 2013). CAD software devel-

opment is speeding up due to the need of less invasive

medical procedures, and MRI analysis has proven

to be effective in tumor diagnosis (Vos et al., 2012;

Hegde et al., 2013; Giannini et al., 2013). Most of the

commercially available CAD solutions, like the Dy-

naCAD by Invivo(Invivo, 2013), are not designed and

developed to be integrated with new features. More-

over, most of them comes with the hardware for the

MRI.

On the open source side, a wide community of

developers has been involved in several open source

projects to develop libraries, development frame-

works, and software tools. Some of them, like the

3DSlicer framework (3DSlicer, 2013), are very popu-

lar among researchers because they provide lots of al-

gorithms and ﬁlters to easily manipulate medical im-

ages (Pieper et al., 2006; Hegde et al., 2013; Pen-

zkofer and Tempany-Afdhal, 2013). Unfortunately,

they lack of simplicity when the ﬁnal goal is building

a CAD software with a custom Graphical User Inter-

face (GUI). In fact, very effectiveframeworks, such as

3DSlicer, that provide GUI capability, are not meant

to build that GUI from scratch (by forcing the user to

interact with a standardized interface). On the other

hand, looking for a modular and ﬂexible software ar-

chitecture design and implementation, which requires

to plug new features in when a new algorithm or a fea-

ture will be proposed, introduce a challenging com-

plexity in the software design that can take advantage

from frameworks and libraries designed for that pur-

pose. Finally, all available tools are not designed with

a clinical work-ﬂow in mind.

In this paper, we present a CAD software currently

under development by two research institutions, the

Institute for Cancer Research and Treatment of Can-

diolo (TO) and the Politecnico of Torino. The idea

is to develop a complete tool for assisted diagnosis,

from the patient data management to the ﬁnal medical

report, that includes a probabilistic malignancyclassi-

ﬁcation of tumor tissue by imaging. The tool meets all

modern software paradigms, in terms of multi-tasking

and multi-platform solutions. By overcoming the lim-

itations of the current subjectiveway of analyzing MR

data, this approach could substantially improvethe di-

agnostic and therapeutic work-up of individuals with

PCa, and, in the end, dramatically improve patients’

quality of life.

The paper presents the tool by describing the

work-ﬂow used to design the software (Section 2).

Section 3 introduces the software architecture design.

Eventually, Section 4 gives some conclusion and fu-

ture perspectives for the tool development.

2 THE SOFTWARE WORK-FLOW

The idea of our CAD software was to design a tool

to support the diagnosis process by providing a built-

in MRI multi-volumes view, including a malignancy

probability map. It had to implement a comprehen-

sive work-ﬂow able to guide the physician from im-

age upload to the ﬁnal medical report.

The software work-ﬂow design requires to sim-

plify the user experience, by drawing sequences of

operations that quickly reach what the user is ex-

pected to do. Design very complex work-ﬂows leads

to a huge variety of behaviors the software is going to

have to manage. This huge variety commonly comes

with low usability of the software due to the difﬁculty

to prevent all possible combinations of those behav-

iors. General purpose software may take advantage

of a wider ﬂexibility given by lots of possible com-

binations of operations, but specialistic software do

not. Specialistic softwares simple work-ﬂow leading

to small set of well-known behaviors that facilitate the

software usability and learning curve. In our case, we

have to keep the user focused on the main tasks the

software is designed for.

In particular, the classiﬁcation process requires a

deep analysis to be included in the software design, in

particular, by identifying the correct way to integrate

it in the work-ﬂow and by designing the proper user

interaction with the classiﬁcation output. The ﬁnal

work-ﬂow is designed with two main goals:

1. The patient management, where the user has ei-

BIOIMAGING2014-InternationalConferenceonBioimaging

ther to import new patients to be diagnosed or to

manage already in-platform ones.

2. The diagnosis work-up, where the physician has

to be guided from the patient analysis (through the

visual analysis of MRI volumes and classiﬁcation

results) to the patient medical report (useful either

for further analyses or for pre-surgical reports).

This entire work-ﬂow is shown in Figure 1.

Action

Selection

Analysis

View

Import

Patient

Patient Folder

Analysis

Patient

Diagnosis

Patient Information

Edit

Import

Patient

Manage

Patient

Medical Report

View

Manage

Patient

MRI Analysis &

Malignancy

Classification

Patient Information

Update

Figure 1: The Software Work-Flow in details.

The patient management path is the simplest one,

and can be split in two possible ﬂows: (i) the patient

import and (ii) the already in-platform patients man-

agement. We start focusing on the ﬁrst one.

Importing a patient means, at ﬁrst, being able to

analyze a MRI folder, which contains all image vol-

umes generated by the MRI machine for that patient

exam, and identify all available volumes, providing

the user with their full list. Then, the user can choose

the set of volumes to be imported so the import pro-

cess starts. During the import process, all imaging

analysis algorithms run, including registration stages,

multi-parametric analysis and the classiﬁcation pro-

cess (Giannini et al., 2013), and the physician has to

enter all information about the patient (e.g., blood test

results and so on). Only when all the analyses are

completed the user will be able to conclude the im-

port.

The management of in-platform patients allows to

modify all previously entered data and to add a new

MRI exam for a given patient. In this case, the work-

ﬂow is an alternative case of the import one: only the

MRI folder has to be analyzed and, if required, the

patient information has to be updated (e.g., if the new

exam comes with new blood tests).

The diagnosis work-up is the most ﬂexible and

the most strict at the same time. The classiﬁcation

process requires a deep analysis to be included in the

software design, in particular, by identifying the cor-

rect way to integrate it in the work-ﬂow and by de-

signing the proper user interaction with the classiﬁ-

cation output.Once a patient is selected, the radiolo-

gist can select the MRI volumes to be displayed, to-

gether with the probability map superimposed to the

T2-w axial MRI image (see Figure 2). Then the work-

ﬂow lets the physician analyze all data, by navigating

volumes, i.e., in single or multi view mode. In the

multi-view mode, the diagnosis is aided by accessing

up to four views simultaneously, which may include

any of the performed MR sequences, such as DCE

volumes, DWI volumes and so on. Basic image nav-

igation tools like zoom, pan and re-slice need to be

available for each volume and time-related volumes

(such as DCE) navigation may be either space or time

dependent.

Figure 2: The prototyped GUI: the probability map can be

noticed on the ﬁrst quadrant of the four images view, and

the related colors are on the right sidebar.

During the diagnosis, the user is supported by a

set of image interacting tools, such as deﬁne hand-

free and circular Region of Interests (ROIs), and a set

of extra elaborations among images, such as retrieve

wash-in and wash-out plots of ROIs. Physicians may

add annotations free-text or predeﬁned text related to

one of the 16 prostatic regions of interest, as previ-

ously reported (Dickinson et al., 2011), and create

screenshots of peculiar image areas (see Figure 3).

Figure 3: The prototyped GUI: annotations form is on the

right sidebar.

Once the physician analysis is completed, a report

form will be available to provide the ﬁnal medical re-

AProstateCancerComputerAidedDiagnosisSoftwareincluding

MalignancyTumorProbabilisticClassification

port. The form is provided by a set of common word-

processor tools to write the report part. All previous

annotations and screenshots can be linked in the re-

port, and an interactive 16 prostatic regions of interest

map allows the physician to better annotate the tumor

morphological position. The report is included in the

data stored for each patient.

3 THE SOFTWARE

ARCHITECTURE

The software architecture has been designed to rely

on two main modules: the internal core engine and

the visualization engine (Figure 4). The internal core

engine implements all MR images manipulation, clas-

siﬁcation, and data analysis processes. The visual-

ization engine deﬁnes all graphic aspects from the

GUI interaction to the graphical rendering of MRI

volumes. We chose to implement the internal core en-

gine using Insight Toolkit (ITK) (Kitware, 2013a) and

Boost (Dawes et al., 2013) libraries, while the visual-

ization engine is a QT-based (Digia plc, 2013) appli-

cation supported by the Visualization Toolkit (VTK)

library (Kitware, 2013b). They have been chosen be-

cause they all are multi-platform libraries. Both en-

gines are based on a pipeline execution paradigm, in-

herited from ITK and VTK principles (Pieper et al.,

2006).

Communication

Channel

Internal Core

Engine

Visualization

Engine

XML

Engine

XML

Data

MRI

Volumes

Data

Malignancy

Classification

Registration

Algorithms

…

Figure 4: The Software Architecture exploited.

Since the software is an on-going research tool,

we kept the internal core engine implementation,

whenever possible, modular. This way, new al-

gorithms can be developed and plugged-in without

rewriting the software. The typical modular approach

of development framework, like 3DSlicer, is reached

by building an interpreter engine above the real ex-

ecution engine. Due to the interpretation step re-

quired, performances may suffer of this kind of ar-

chitectural implementation. In our case, we chose

to resort to a modular approach by exploiting the

C++ template mechanism. We designed all core pro-

cesses, such as the classiﬁcation process, as template-

based processes: the implementation of new algo-

rithms will respond to their compliance with the tem-

plate paradigm. In particular, for the classiﬁcation

process, which is the most innovativeparts of the soft-

ware, we carefully deﬁned common input and output

formats. Thus, changes in the classiﬁcation strategy

can be implemented by new modules without requir-

ing changes in the execution pipeline. All registration

algorithms (involved in the patient analysis) are also

modular, and they can be updated or modiﬁed resort-

ing to the same approach.

It can be said that modern algorithms, especially

biological and imaging ones, require a high paral-

lel implementation in order to cope with complex

tasks and to be solved in a reasonable time (Tucker-

man et al., 2000; Glockner et al., 2005). The imag-

ing and the classiﬁcation algorithms heavily suffer of

time consuming issues that have been addressed by all

modern computational techniques to speed them up,

such as multi-core or multi-GPUs implementations

(Baskaran et al., 2010; Vasanawala et al., 2011). Even

so, the user interaction itself requires the software to

be everresponsive(Betz et al., 2000). To do so the im-

plementation has to resort to parallel events and tasks.

Using Boost and ITK multi-threading capabilities, we

have been able to provide the internal core engine of

a parallel architecture and implementation. Resorting

to QT, ITK and VTK events management paradigm,

we design an event-based communication channel to

connect the two cores. All parallel tasks are synchro-

nized using the communication channel: when an in-

ternal core engine task starts, i.e. an imaging algo-

rithm, the visualization engine is still able to provide

the user a continuous interaction. Once the task emits

a completion event, the communication channel takes

care of the event itself and forwards it to the visualiza-

tion engine, which can behaves as needed. The com-

munication channel assures the responsiveness of the

GUI by including in the software architecture an in-

termediate level that takes care of all time consuming

algorithms.

A ﬁnal aspect we deﬁned in the architecture is

the data infrastructure and management. Since the

amount of data required in a diagnostic tool is a huge

one (tons of images and patient information), data

have to be manipulated with ease. Because the devel-

BIOIMAGING2014-InternationalConferenceonBioimaging

opment of a full-feature version of the software may

take lots of time, the data have to be modiﬁed also

outside the software, i.e. by hand, both in terms of

schema and information (e.g., a new patient added,

a new set of information previously missing). More-

over, most of the data is a sensible one, so the data

management has to provide anonymizing features. At

this point, we mainly have two different choices: (i) a

relational database management system (RDBMS) or

(ii) an XML data management system.

XML and RDBMS are often mixed up to-

gether (Paoin and Boonchai-Apisit, 2009; Taghva

and Jayakumar, 2009) because XML allows to eas-

ily exchange data or to deﬁne very complex queries

(more than SQL does in some contexts, e.g., bibliog-

raphy searching) whereas relational databases orga-

nize, store and access data in a more efﬁcient way.

Providing our software with a RDBMS would require

including in the software architecture an external en-

gine to manage a small part of the data: the patient

information. In fact, all imaging-related data, such

as MRI volumes, is not conveniently stored in a re-

lational database. Nevertheless, RDBMS data do not

come in a human-readable (due to data compression

and indexes management), so it ever requires an in-

termediate level of translation to be modiﬁed by the

user. Instead, XML data is a more human-readable

format, which can easily handle all patient data and

still permits a direct modiﬁcation, even outside the

tool. From the architectural point of view, XML en-

gines are usually software classes that can be included

in a software project. In fact, QT contains several dif-

ferent libraries to manipulate XML-based documents.

Thus, we chose to add a XML engine to manipulate

an XML-based database. Since the XML engine is

implemented in QT, it can be plugged in the commu-

nication channel without further modiﬁcations.

Conﬁdentiality has been reached by providing the

engine of an encryption module, and by properly de-

signing the GUI not to show the patient’s sensible in-

formation. Eventually, the XML engine also imple-

ment error handling to avoid difﬁculties during the

user testing phase of the project. Whenever a physi-

cian involved in that phase will corrupt the data for

any reason, it should be easy to solve the problem.

It can be seen that, at architectural level, we

reached a very ﬂexible organization, taking care of

the further implementation of new functionalities that

may arise from the research ﬁeld.

4 CONCLUSIONS

In this paper we give a comprehensive description of

the tool we are working on. The methodological ap-

proach for all the design aspects has been exploited

in order to give a better insight of the tool function-

alities. Although the software is under development

and still not available outside the IRCC Institute, the

ﬁrst versions and demos show a promising scenario.

As soon as the main functionality will be considered

as stable, further versions will see a preliminary in-

ﬁeld test, allowing the use in the diagnostic work-up.

At that development stage we plan to have a big mile-

stone review of the project, including physicians feed-

backs and intensive multi-platform performances test.

The software architecture designed guarantees

that the software lifetime can be extended by eas-

ily adding new modules and partially re-programming

the internal core pipeline. The prototyping approach

is going to be the project guideline for further CAD

projects, contributing to help time and cost reduction.

Eventually, once we’ll reach a proper develop-

ment stage, we plan to further evaluate all feasible

contributions to the ITK and VTK libraries in terms

of modules to be proposed to the community.

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