Andrea Damiani

An umbrella protocol for standardized data collection (SDC) in rectal cancer: A prospective uniform naming and procedure convention to support personalized medicine

Predictive models allow treating physicians to deliver tailored treatment moving from prescription by consensus to prescription by numbers. The main features of an umbrella protocol for standardizing data and procedures to create a consistent dataset useful to obtain a trustful analysis for a Decision Support System for rectal cancer are reported.

Recommendations on how to establish evidence from auto-segmentation software in radiotherapy

http://dx.doi.org/10.1016/j.radonc.2014.09.014 0167-8140/ 2014 Elsevier Ireland Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). ⇑ Corresponding author. Address: Radiation Oncology Department – Gemelli ART, Università Cattolica del Sacro Cuore, Largo A. Gemelli, 1, 00168 Rome, Italy. E-mail address: lucaboldrini@hotmail.it (L. Boldrini). Vincenzo Valentini , Luca Boldrini a,⇑, Andrea Damiani , Ludvig P. Muren c

ENT COBRA (Consortium for Brachytherapy Data Analysis): Interdisciplinary standardized data collection system for head and neck patients treated with interventional radiotherapy (brachytherapy)

Purpose Aim of the COBRA (Consortium for Brachytherapy Data Analysis) project is to create a multicenter group (consortium) and a web-based system for standardized data collection. Material and methods GEC-ESTRO (Groupe Européen de Curiethérapie – European Society for Radiotherapy & Oncology) Head and Neck (H&N) Working Group participated in the project and in the implementation of the consortium agreement, the ontology (data-set) and the necessary COBRA software services as well as the peer reviewing of the general anatomic site-specific COBRA protocol. The ontology was defined by a multicenter task-group. Results Eleven centers from 6 countries signed an agreement and the consortium approved the ontology. We identified 3 tiers for the data set: Registry (epidemiology analysis), Procedures (prediction models and DSS), and Research (radiomics). The COBRA-Storage System (C-SS) is not time-consuming as, thanks to the use of “brokers”, data can be extracted directly from the single centers storage systems through a connection with “structured query language database” (SQL-DB), Microsoft Access®, FileMaker Pro®, or Microsoft Excel®. The system is also structured to perform automatic archiving directly from the treatment planning system or afterloading machine. The architecture is based on the concept of “on-purpose data projection”. The C-SS architecture is privacy protecting because it will never make visible data that could identify an individual patient. This C-SS can also benefit from the so called “distributed learning” approaches, in which data never leave the collecting institution, while learning algorithms and proposed predictive models are commonly shared. Conclusions Setting up a consortium is a feasible and practicable tool in the creation of an international and multi-system data sharing system. COBRA C-SS seems to be well accepted by all involved parties, primarily because it does not influence the centers own data storing technologies, procedures, and habits. Furthermore, the method preserves the privacy of all patients.

Moddicom: a Complete and Easily Accessible Library for Prognostic Evaluations Relying on Image Features

Decision Support Systems (DSSs) are increasingly exploited in the area of prognostic evaluations. For predicting the effect of therapies on patients, the trend is now to use image features, i.e. information that can be automatically computed by considering images resulting by analysis. The DSSs application as predictive tools is particularly suitable for cancer treatment, given the peculiarities of the disease -which is highly localised and lead to significant social costs- and the large number of images that are available for each patient. At the state of the art, there exists tools that allow to handle image features for prognostic evaluations, but they are not designed for medical experts. They require either a strong engineering or computer science background since they do not integrate all the required functions, such as image retrieval and storage. In this paper we fill this gap by proposing Moddicom, a user-friendly complete library specifically designed to be exploited by physicians. A preliminary experimental analysis, performed by a medical expert that used the tool, demonstrates the efficiency and the effectiveness of Moddicom.

Distributed Learning to Protect Privacy in Multi-centric Clinical Studies

Research in medicine has to deal with the growing amount of data about patients which are made available by modern technologies. All these data might be used to support statistical studies, and for identifying causal relations. To use these data, which are spread across hospitals, efficient merging techniques as well as policies to deal with this sensitive information are strongly needed. In this paper we introduce and empirically test a distributed learning approach, to train Support Vector Machines (SVM), that allows to overcome problems related to privacy and data being spread around. The introduced technique allows to train algorithms without sharing any patients-related information, ensuring privacy and avoids the development of merging tools. We tested this approach on a large dataset and we described results, in terms of convergence and performance; we also provide considerations about the features of an IT architecture designed to support distributed learning computations.

Adaptive optimization by 6 DOF robotic couch in prostate volumetric IMRT treatment: rototranslational shift and dosimetric consequences.

The purpose of this study was to investigate the magnitude and dosimetric relevance of translational and rotational shifts on IGRT prostate volumetric-modulated arc therapy (VMAT) using Protura six degrees of freedom (DOF) Robotic Patient Positioning System. Patients with cT3aN0M0 prostate cancer, treated with VMAT simultaneous integrated boost (VMAT-SIB), were enrolled. PTV2 was obtained adding 0.7 cm margin to seminal vesicles base (CTV2), while PTV1 adding to prostate (CTV1) 0.7 cm margin in all directions, except 1.2 cm, as caudal margin. A daily CBCT was acquired before dose delivery. The translational and rotational displacements were corrected through Protura Robotic Couch, collected and applied to the simulation CT to obtain a translated CT (tCT) and a rototranslated CT (rtCT) on which we recalculated the initial treatment plan (TP). We analyzed the correlation between dosimetric coverage, organs at risk (OAR) sparing, and translational or rotational displacements. The dosimetric impact of a rototranslational correction was calculated. From October 2012 to September 2013, a total of 263 CBCT scans from 12 patients were collected. Translational shifts were <5mm in 81% of patients and the rotational shifts were <2∘ in 93% of patient scans. The dosimetric analysis was performed on 172 CBCT scans and calculating 344 VMAT-TP. Two significant linear correlations were observed between yaw and the V20 femoral heads and between pitch rotation and V50 rectum (p<0.001); rototranslational correction seems to impact more on PTV2 than on PTV1, especially when margins are reduced. Rotational errors are of dosimetric significance in sparing OAR and in target coverage. This is relevant for femoral heads and rectum because of major distance from isocenter, and for seminal vesicles because of irregular shape. No correlation was observed between translational and rotational errors. A study considering the intrafractional error and the deformable registration is ongoing. PACS number: 87.55.de.The purpose of this study was to investigate the magnitude and dosimetric relevance of translational and rotational shifts on IGRT prostate volumetric‐modulated arc therapy (VMAT) using Protura six degrees of freedom (DOF) Robotic Patient Positioning System. Patients with cT3aN0M0 prostate cancer, treated with VMAT simultaneous integrated boost (VMAT‐SIB), were enrolled. PTV2 was obtained adding 0.7 cm margin to seminal vesicles base (CTV2), while PTV1 adding to prostate (CTV1) 0.7 cm margin in all directions, except 1.2 cm, as caudal margin. A daily CBCT was acquired before dose delivery. The translational and rotational displacements were corrected through Protura Robotic Couch, collected and applied to the simulation CT to obtain a translated CT (tCT) and a rototranslated CT (rtCT) on which we recalculated the initial treatment plan (TP). We analyzed the correlation between dosimetric coverage, organs at risk (OAR) sparing, and translational or rotational displacements. The dosimetric impact of a rototranslational correction was calculated. From October 2012 to September 2013, a total of 263 CBCT scans from 12 patients were collected. Translational shifts were <5mm in 81% of patients and the rotational shifts were <2∘ in 93% of patient scans. The dosimetric analysis was performed on 172 CBCT scans and calculating 344 VMAT‐TP. Two significant linear correlations were observed between yaw and the V20 femoral heads and between pitch rotation and V50 rectum (p<0.001); rototranslational correction seems to impact more on PTV2 than on PTV1, especially when margins are reduced. Rotational errors are of dosimetric significance in sparing OAR and in target coverage. This is relevant for femoral heads and rectum because of major distance from isocenter, and for seminal vesicles because of irregular shape. No correlation was observed between translational and rotational errors. A study considering the intrafractional error and the deformable registration is ongoing. PACS number: 87.55.de

pMineR: An Innovative R Library for Performing Process Mining in Medicine

Process Mining is an emerging discipline investigating tasks related with the automated identification of process models, given real-world data (Process Discovery). The analysis of such models can provide useful insights to domain experts. In addition, models of processes can be used to test if a given process complies (Conformance Checking) with specifications. For these capabilities, Process Mining is gaining importance and attention in healthcare.

Radiomics for rectal cancer

Diagnosis and treatment of locally advanced rectal cancer is mainly based on multimodal approach for staging, planning and treatment. The modern radiological and imaging techniques offer, day after day, the possibility to characterize tumor lesions in a more precise and prognostically valuable way. In rectal cancer, extending often the characterization to colon cancer, literature offers some evidences that quantitative and “radiomics” analysis of tumor images might improve the prognostic evaluation of the tumor and the patients’ characterization. Unfortunately, as in other fields of radiomics, the rise of new evidence and models based on single institution case series don’t offer the practical chance to apply them universal data set. Greater efforts in the direction of model evaluation and validation, above all using an external validation approach, are expected to be shown in the coming years for validation of methodology.

Assessing the conformity to clinical guidelines in oncology: An example for the multidisciplinary management of locally advanced colorectal cancer treatment

Purpose The purpose of this paper is to describe a methodology to deal with conformance checking through the implementation of computer-interpretable-clinical guidelines (CIGs), and present an application of the methodology to real-world data and a clinical pathway for radiotherapy-related oncological treatment. Design/methodology/approach This methodology is implemented by a software able to use the hospital electronic health record data to assess the adherence of the actual executed clinical processes to a clinical pathway, monitoring at the same time management-related efficiency and performance parameters, and ideally, suggesting ways to improve them. Findings Three use cases are presented, in which the results of conformance checking are used to compare different branches of the executed guidelines with respect to the adherence to ideal process, temporal distribution of state-to-state transitions, and overall treatment efficacy, in order to extract data-driven evidence that could be of interest for the hospital management. Originality/value This approach has the result of applying management-oriented data mining technique on sequential data, typical of process mining, to the result of a conformity check between the preliminary knowledge defined by clinicians and the real-world data, typical of CIGs.

Preliminary Data Analysis in Healthcare Multicentric Data Mining: a Privacy-preserving Distributed Approach

The new era of cognitive health care systems offers a large amount of patient data that can be used to develop prediction models and clinical decision support systems. In this frame, the multi-institutional approach is strongly encouraged in order to reach more numerous samples for data mining and more reliable statistics. For these purposes, shared ontologies need to be developed for data management to ensure database semantic coherence in accordance with the various centers’ ethical and legal policies. Therefore, we propose a privacy-preserving distributed approach as a preliminary data analysis tool to identify possible compliance issues and heterogeneity from the agreed multi-institutional research protocol before training a clinical prediction model. This kind of preliminary analysis appeared fast and reliable and its results corresponded to those obtained using the traditional centralized approach. A real time interactive dashboard has also been presented to show analysis results and make the workflow swifter and easier.