Immunohistochemical expression of HMGB1 and related proteins in the skin as a possible tool for determining post-mortem interval: a preclinical study

Determining the post-mortem interval (PMI) is one of forensic pathology's primary objectives and one of its most challenging tasks. Numerous studies have demonstrated the accuracy of histomorphology and immunohistochemical investigations in determining the time of death. Nevertheless, the skin, a robust and easy-to-remove tissue, has only been partially analyzed so far. By studying 20 adult male mice, we tried to determine whether post-mortem immunohistochemical detection in the skin of HMGB1 proteins and associated components (Beclin1 and RAGE) could be used for this purpose. We discovered that nuclear HMGB1 overexpression indicates that death occurred within the previous 12 h, nuclear HMGB1 negativization with high cytoplasmic HMGB1 intensity indicates that death occurred between 12 and 36 h earlier and cytoplasmic HMGB1 negativization indicates that more than 48 h have passed since death. RAGE and Beclin1 levels in the cytoplasm also decreased with time. The latter proteins' negativization might indicate that more than 24 and 36 h, respectively, have passed from the time of death. These indicators might potentially be helpful in forensic practice for determining the PMI using immunohistochemistry.

Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching "chronic myeloid leukemia" and "artificial intelligence". The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the 'human' factor, which remains key in this domain.

Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review

BACKGROUND

In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting.

METHODS

A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases.

RESULTS

Seventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at 18 F-FDG PET, evaluation of cytologically indeterminate thyroid nodules, assessment of thyroid cancer and classification of thyroid diseases using nuclear medicine techniques.

CONCLUSION

Despite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.

Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study

Immunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge. We analyzed the association between several radiomics features and response to immunotherapy in 53 patients treated with checkpoint inhibitors for advanced renal cell carcinoma. We found that the following features are associated with progression of disease as best tumor response: F_stat.range (p < .0004), F_stat.max (p < .0007), F_stat.var (p < .0016), F_stat.uniformity (p < .0020), F_stat.90thpercentile (p < .0050). Gross tumor volumes characterized by high values of F_stat.var and F_stat.max (greater than 60,000 and greater than 300, respectively) are most likely related to a high risk of progression. Further analyses are warranted to confirm these results. Radiomics, together with other potential predictive factors, such as gut microbiota, genetic features or circulating immune molecules, could allow a personalized treatment for patients with advanced renal cell carcinoma.

Semiautomated Pipeline to Quantify Tumor Evolution From Real-World Positron Emission Tomography/Computed Tomography Imaging

PURPOSE

A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology.

METHODS

We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports.

RESULTS

The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P = .89).

CONCLUSION

We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.

Baseline 18F-FDG PET/CT Radiomics in Classical Hodgkin's Lymphoma: The Predictive Role of the Largest and the Hottest Lesions

This study investigated the predictive role of baseline 18F-FDG PET/CT (bPET/CT) radiomics from two distinct target lesions in patients with classical Hodgkin's lymphoma (cHL). cHL patients examined with bPET/CT and interim PET/CT between 2010 and 2019 were retrospectively included. Two bPET/CT target lesions were selected for radiomic feature extraction: Lesion_A, with the largest axial diameter, and Lesion_B, with the highest SUVmax. Deauville score at interim PET/CT (DS) and 24-month progression-free-survival (PFS) were recorded. Mann-Whitney test identified the most promising image features (p < 0.05) from both lesions with regards to DS and PFS; all possible radiomic bivariate models were then built through a logistic regression analysis and trained/tested with a cross-fold validation test. The best bivariate models were selected based on their mean area under curve (mAUC). A total of 227 cHL patients were included. The best models for DS prediction had 0.78 ± 0.05 maximum mAUC, with a predominant contribution of Lesion_A features to the combinations. The best models for 24-month PFS prediction reached 0.74 ± 0.12 mAUC and mainly depended on Lesion_B features. bFDG-PET/CT radiomic features from the largest and hottest lesions in patients with cHL may provide relevant information in terms of early response-to-treatment and prognosis, thus representing an earlier and stronger decision-making support for therapeutic strategies. External validations of the proposed model are planned.

Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients' quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients' characteristics.

METHODS

We consider data collected in two distinct data sources, namely the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset and a real-world clinical register (ALS-BS) including data of patients followed up in two tertiary clinical centers of Brescia (Italy). With a focus on the functional abilities progressively impaired as the disease progresses, we use two Process Discovery methods, namely the Directly-Follows Graph and the CareFlow Miner, to mine the population disease trajectories on the PRO-ACT dataset. We characterize the impairment trajectories in terms of patterns, timing, and probabilities, and investigate the effect of some patients' characteristics at onset on the followed paths. Finally, we perform a comparative study of the impairment trajectories mined in PRO-ACT versus ALS-BS.

RESULTS

We delineate the progression pathways on PRO-ACT, identifying the predominant disabilities at different stages of the disease: for instance, 85% of patients enter the trials without disabilities, and 48% of them experience the impairment of Walking/Self-care abilities first. We then test how a spinal onset increases the risk of experiencing the loss of Walking/Self-care ability as first impairment (52% vs. 27% of patients develop it as the first impairment in the spinal vs. the bulbar cohorts, respectively), as well as how an older age at onset corresponds to a more rapid progression to death. When compared, the PRO-ACT and the ALS-BS patient populations present some similarities in terms of natural progression of the disease, as well as some differences in terms of observed trajectories plausibly due to the trial scheduling and recruitment criteria.

CONCLUSIONS

We exploited PM to provide an overview of the evolution scenarios of an ALS trial population and to preliminary compare it to the progression observed in a clinical cohort. Future work will focus on further improving the understanding of the disease progression mechanisms, by including additional real-world subjects as well as by extending the set of events considered in the impairment trajectories.

Interactive process mining of cancer treatment sequences with melanoma real-world data

The growing availability of clinical real-world data (RWD) represents a formidable opportunity to complement evidence from randomized clinical trials and observe how oncological treatments perform in real-life conditions. In particular, RWD can provide insights on questions for which no clinical trials exist, such as comparing outcomes from different sequences of treatments. To this end, process mining is a particularly suitable methodology for analyzing different treatment paths and their associated outcomes. Here, we describe an implementation of process mining algorithms directly within our hospital information system with an interactive application that allows oncologists to compare sequences of treatments in terms of overall survival, progression-free survival and best overall response. As an application example, we first performed a RWD descriptive analysis of 303 patients with advanced melanoma and reproduced findings observed in two notorious clinical trials: CheckMate-067 and DREAMseq. Then, we explored the outcomes of an immune-checkpoint inhibitor rechallenge after a first progression on immunotherapy versus switching to a BRAF targeted treatment. By using interactive process-oriented RWD analysis, we observed that patients still derive long-term survival benefits from immune-checkpoint inhibitors rechallenge, which could have direct implications on treatment guidelines for patients able to carry on immune-checkpoint therapy, if confirmed by external RWD and randomized clinical trials. Overall, our results highlight how an interactive implementation of process mining can lead to clinically relevant insights from RWD with a framework that can be ported to other centers or networks of centers.

Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer?

Background: We evaluated the value of pre-treatment positron-emission tomography−computed tomography (PET-CT)-based radiomic features in predicting the locoregional progression-free survival (LR-PFS) of patients with inoperable or unresectable oesophageal cancer. Material and Methods: Forty-six patients were included and 230 radiomic parameters were extracted. After a principal component analysis (PCA), we identified the more robust radiomic parameters, and we used them to develop a heatmap. Finally, we correlated these radiomic features with LR-PFS. Results: The median follow-up time was 17 months. The two-year LR-PFS and PFS rates were 35.9% (95% CI: 18.9−53.3) and 21.6% (95%CI: 10.0−36.2), respectively. After the correlation analysis, we identified 55 radiomic parameters that were included in the heatmap. According to the results of the hierarchical clustering, we identified two groups of patients presenting statistically different median LR-PFSs (22.8 months vs. 9.9 months; HR = 2.64; 95% CI 0.97−7.15; p = 0.0573). We also identified two radiomic features (“F_rlm_rl_entr_per” and “F_rlm_2_5D_rl_entr”) significantly associated with LR-PFS. Patients expressing a “F_rlm_2_5D_rl_entr” of <3.3 had a better median LR- PFS (29.4 months vs. 8.2 months; p = 0.0343). Patients presenting a “F_rlm_rl_entr_per” of <4.7 had a better median LR-PFS (50.4 months vs. 9.9 months; p = 0.0132). Conclusion: We identified two radiomic signatures associated with a lower risk of locoregional relapse after CRT.

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital “Istituti Clinici Salvatore Maugeri” in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

Comparison between Two Different Scanners for the Evaluation of the Role of 18F-FDG PET/CT Semiquantitative Parameters and Radiomics Features in the Prediction of Final Diagnosis of Thyroid Incidentalomas

The aim of this study was to compare two different tomographs for the evaluation of the role of semiquantitative PET/CT parameters and radiomics features (RF) in the prediction of thyroid incidentalomas (TIs) at ¹⁸F-FDG imaging. A total of 221 patients with the presence of TIs were retrospectively included. After volumetric segmentation of each TI, semiquantitative parameters and RF were extracted. All of the features were tested for significant differences between the two PET scanners. The performances of all of the features in predicting the nature of TIs were analyzed by testing three classes of final logistic regression predictive models, one for each tomograph and one with both scanners together. Some RF resulted significantly different between the two scanners. PET/CT semiquantitative parameters were not able to predict the final diagnosis of TIs while GLCM-related RF (in particular GLCM entropy_log2 e GLCM entropy_log10) together with some GLRLM-related and GLZLM-related features presented the best predictive performances. In particular, GLCM entropy_log2, GLCM entropy_log10, GLZLM SZHGE, GLRLM HGRE and GLRLM HGZE resulted the RF with best performances. Our study enabled the selection of some RF able to predict the final nature of TIs discovered at ¹⁸F-FDG PET/CT imaging. Classic semiquantitative and volumetric PET/CT parameters did not reveal these abilities. Furthermore, a good overlap in the extraction of RF between the two scanners was underlined.

A differential process mining analysis of COVID-19 management for cancer patients

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care.

Role of 18F-FDG PET/CT Radiomics Features in the Differential Diagnosis of Solitary Pulmonary Nodules: Diagnostic Accuracy and Comparison between Two Different PET/CT Scanners

The aim of this retrospective study was to investigate the ability of 18 fluorine-fluorodeoxyglucose positron emission tomography/CT (¹⁸F-FDG-PET/CT) metrics and radiomics features (RFs) in predicting the final diagnosis of solitary pulmonary nodules (SPN). We retrospectively recruited 202 patients who underwent a ¹⁸F-FDG-PET/CT before any treatment in two PET scanners. After volumetric segmentation of each lung nodule, 8 PET metrics and 42 RFs were extracted. All the features were tested for significant differences between the two PET scanners. The performances of all features in predicting the nature of SPN were analyzed by testing three classes of final logistic regression predictive models: two were built/trained through exploiting the separate data from the two scanners, and the other joined the data together. One hundred and twenty-seven patients had a final diagnosis of malignancy, while 64 were of a benign nature. Comparing the two PET scanners, we found that all metabolic features and most of RFs were significantly different, despite the cross correlation being quite similar. For scanner 1, a combination between grey level co-occurrence matrix (GLCM), histogram, and grey-level zone length matrix (GLZLM) related features presented the best performances to predict the diagnosis; for scanner 2, it was GLCM and histogram-related features and metabolic tumour volume (MTV); and for scanner 1 + 2, it was histogram features, standardized uptake value (SUV) metrics, and MTV. RFs had a significant role in predicting the diagnosis of SPN, but their accuracies were directly related to the scanner.

Late-onset and long-lasting immune-related adverse events from immune checkpoint-inhibitors: An overlooked aspect in immunotherapy

BACKGROUND

Immune checkpoint inhibitors (ICIs) have revolutionised cancer therapy but frequently cause immune-related adverse events (irAEs). Description of late-onset and duration of irAEs in the literature is often incomplete.

METHODS

To investigate reporting and incidence of late-onset and long-lasting irAEs, we reviewed all registration trials leading to ICI's approval by the US FDA and/or EMA up to December 2019. We analysed real-world data from all lung cancer (LC) and melanoma (Mel) patients treated with approved ICIs at the University Hospital of Lausanne (CHUV) from 2011 to 2019. To account for the immortal time bias, we used a time-dependent analysis to assess the potential association between irAEs and overall survival (OS).

RESULTS

Duration of irAEs and proportion of patients with ongoing toxicities at data cut-off were not specified in 56/62 (90%) publications of ICIs registration trials. In our real-world analysis, including 437 patients (217 LC, 220 Mel), 229 (52.4%) experienced at least one grade ≥2 toxicity, for a total of 318 reported irAEs, of which 112 (35.2%) were long-lasting (≥6 months) and about 40% were ongoing at a median follow-up of 369 days [194-695] or patient death. The cumulative probability of irAE onset from treatment initiation was 42.8%, 51.0% and 57.3% at 6, 12 and 24 months, respectively. The rate of ongoing toxicity from the time of first toxicity onset was 42.8%, 38.4% and 35.7% at 6, 12 and 24 months. Time-dependent analysis showed no significant association between the incidence of irAEs and OS in both cohorts (log Rank p = 0.67 and 0.19 for LC and Mel, respectively).

CONCLUSIONS

Late-onset and long-lasting irAEs are underreported but common events during ICIs therapy. Time-dependent survival analysis is advocated to assess their impact on OS. Real-world evidence is warranted to fully capture and characterise late-onset and long-lasting irAEs in order to implement appropriate strategies for patient surveillance and follow-up.

Process mining to optimize palliative patient flow in a high-volume radiotherapy department

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Management of palliative patients can be often out of standard clinical pathways.

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Process mining methodology has still not been exploited for palliative patients.

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Process discovery of event-log highlighted current workflow complexity/ weaknesses.

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Conformance checking evaluated how a set of events-log flow through a given model.

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Palliative patient patterns of care can be tracked and monitored by process mining.

Introduction

In radiotherapy, palliative patients are often suboptimal managed and patients experience long waiting times. Event-logs (recorded local files) of palliative patients, could provide a continuative decision-making system by means of shared guidelines to improve patient flow. Based on an event-log analysis, we aimed to accurately understand how to successively optimize patient flow in palliative care.

Methods

A process mining methodology was applied on palliative patient flow in a high-volume radiotherapy department. Five hundred palliative radiation treatment plans of patients with bone and brain metastases were included in the study, corresponding to 290 patients treated in our department in 2018. Event-logs and the relative attributes were extracted and organized. A process discovery algorithm was applied to describe the real process model, which produced the event-log. Finally, conformance checking was performed to analyze how the acquired event-log database works in a predefined theoretical process model.

Results

Based on the process discovery algorithm, 53 (10%) plans had a dose prescription of 8 Gy, 249 (49.8%) plans had a dose prescription of 20 Gy and 159 (31.8%) plans had a dose prescription of 30 Gy. The remaining 39 (7.8%) plans had different dose prescriptions. Considering a median value, conformance checking demonstrated that event-logs work in the theoretical model.

Conclusions

The obtained results partially validate and support the palliative patient care guideline implemented in our department. Process mining can be used to provide new insights, which facilitate the improvement of existing palliative patient care flows.

A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer

PURPOSE

Aim of this study was to develop a generalised radiomics model for predicting pathological complete response after neoadjuvant chemo-radiotherapy in locally advanced rectal cancer patients using pre-CRT T2-weighted images acquired at a 1.5 T and a 3 T scanner.

METHODS

In two institutions, 195 patients were scanned: 136 patients were scanned on a 1.5 T MR scanner, 59 patients on a 3 T MR scanner. Gross tumour volumes were delineated on the MR images and 496 radiomic features were extracted, applying the intensity-based (IB) filter. Features were standardised with Z-score normalisation and an initial feature selection was carried out using Wilcoxon-Mann-Whitney test: The most significant features at 1.5 T and 3 T were selected as main features. Several logistic regression models combining the main features with a third one selected by those resulting significant were elaborated and evaluated in terms of area under curve (AUC). A tenfold cross-validation was repeated 300 times to evaluate the model robustness.

RESULTS

Three features were selected: maximum fractal dimension with IB = 0-50, energy and grey-level non-uniformity calculated on the run-length matrix with IB = 0-50. The AUC of the model applied to the whole dataset after cross-validation was 0.72, while values of 0.70 and 0.83 were obtained when 1.5 T and 3 T patients were considered, respectively.

CONCLUSIONS

The model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.

Personalised support of brain tumour patients during radiotherapy based on psychological profile and quality of life

PURPOSE

Psychological distress in primary malignant brain tumour (PMBT) patients is associated with poorer outcomes. Radiotherapy (RT) often induces side effects that significantly influence patients' quality of life (QoL), with potential impact on survival. We evaluated distress, anxiety, depression, and QoL over time to identify patients with difficulties in these areas who required more intense psychological support.

METHODS

Psychological questionnaires-Distress Thermometer (DT), Hospital Anxiety and Depression Scale (HADS), and Functional Assessment of Cancer Therapy (FACT-G and FACT-Br)-were completed at the beginning (T0), in the middle (T1), directly after RT (T2), and 3 months after RT (T3). We personalised the psychological support provided for each patient with a minimum of three sessions ('typical' schedule) and a maximum of eight sessions ('intensive' schedule), depending on the patients' psychological profiles, clinical evaluations, and requests. Patients' survival was evaluated in the glioblastoma multiforme (GBM) patients, with an explorative intent.

RESULTS

Fifty-nine consecutive PMBT patients receiving post-operative RT were included. For patients who were reported as 'not distressed' at T0, no statistically significant changes were noted. In contrast, patients who were 'distressed' at T0 showed statistically significant improvements in DT, HADS, FACT-G, and FACT-Br scores over time. 'Not distressed' patients required less psychological sessions over the study duration than 'distressed' patients. Interestingly, 'not distressed' GBM patients survived longer than 'distressed' GBM patients.

CONCLUSIONS

Increased psychological support improved distress, mood, and QoL for patients identified as 'distressed', whereas psychological well-being was maintained with typical psychological support in patients who were identified as being 'not distressed'. These results encourage a standardisation of psychological support for all RT patients.

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?

Recommendations for enhancing the usability and understandability of process mining in healthcare

Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.

Long-lasting, irreversible and late-onset immune-related adverse events (irAEs) from immune checkpoint inhibitors (ICIs): A real-world data analysis

e15095

Background: ICIs are effective in many tumors, but can cause severe irAEs, with potential long-term impact. Description of irAEs in literature is often insufficient, especially in terms of irAEs duration, and in terms of incidence of late-onset events. Namely, duration of irAEs and proportion of patients (pts) with toxicity still ongoing at data cut-off were not specified in 52 (89.6%) of 58 publications of trials leading to ICIs approval by US FDA and/or EMA up to August 2019. Real-world data are urgently needed to integrate this information. Methods: We retrieved the medical charts of all pts affected by lung cancer (LC) and melanoma (Mel) treated consecutively with approved ICIs at the Centre Hospitalier Universitaire Vaudoise (CHUV) from Jan 2012 to Dec 2019. We collected the following data: treatment setting, ICIs received, duration of each treatment, incidence of any irAEs of grade (G) ≥ 2 (according to CTCAE v5.0), time to onset and duration of irAEs. Pts with follow-up (FU) ≤ 28 days were excluded. Results: 345 pts were included (166 LC, 179 Mel), for a total of 455 ICIs treatment lines. 33 pts (7%, all with Mel) were treated in the adjuvant setting, while all the others were treated for advanced disease. 228 pts (50%) received anti-PD1, 43 (9%) anti-PD-L1, 56 (12%) anti-CTLA-4, 100 (22%) ICI-ICI combinations and 28 (6%) ICI–chemotherapy combinations. Median FU was 369 days (range 29-2903). Median duration of treatment was 71 days (range 1-1625). 196 pts (57%) experienced at least 1 G≥2 irAE for a total of 277 irAEs (22% skin, 18% gastro-intentinal [GI], 13% pneumonitis, 12% thyroid disorders, 11% hyphophisitis and 8% hepatitis). With 122/277 (44%) irAEs still on-going at our data cut-off, median duration of irAEs was 98 days; 104/277 irAEs (37%) lasted more than 6 month, of them 89% were considered irreversible. Out of 76 pts still on ICIs treatment at 6 months, incidence of G≥2 irAE with onset after 6 months was 51% (26% pneumonitis, 26% endocrine disorders, 15% skin disorders, 8% hepatitis and GI). Out of 32 pts still on ICIs treatment at 12 months, incidence of G≥2 irAE with onset after 12 months was 41% (38% pneumonitis, 23% endocrine and 15% skin disorders, 8% hepatitis and GI). Conclusions: Our real-world analysis shows that long-lasting and late-onset irAEs represent an important issue during ICIs treatment. A better description of toxicities in clinical trials is needed to allow a proper evaluation of treatment value and the impact on patients' quality of life.

A process mining approach to real-world advanced melanoma treatments

e22040

Background: Process Mining (PM) is an emerging discipline with promising potential in oncology, which offers a time-oriented perspective on the clinical events that define the patient’s (pts) path of care, from diagnosis to follow-up. PM enables us to 1) build unsupervised models that automatically discover frequent patterns and detect unexpected behaviors, and 2) show the pts flow through predefined models, allowing quantitative assessments of conformance/divergence between reality and expected behaviors or medical guidelines. Methods: We explored the potential of PM to analyze treatments in a real-world melanoma cohort of 212 advanced melanoma pts treated with tyrosine kinase inhibitors (TKI), chemotherapy, or immunotherapy (IO) since 2010. 44% of pts are BRAF mutated, 24% NRAS mutated, and 16% WT. Using PM, we characterize the sequences of treatment according to type of drugs, order and timing of consecutive lines. We use kernel density estimations, Fisher or Wilcoxon tests to assess differences between groups. As main endpoint we assess the overall survival (OS) with respect to specific patterns of care followed by the pts using Kaplan-Meier analysis with log-rank test. Results: This approach provides important self-consistency checks for data and reveals the most frequent treatment sequences, and the associated outcome. The 324 administered treatments, characterized in terms of type and occurrence, are: TKI (23%), antiCTLA4 + antiPD1 (22%), antiPD1 (21%), and antiCTLA4 (20%). The overall 3-year OS is 56.9% (95% CI: 49.2 – 65.8). We identified 50 distinct patterns, which can be further aggregated into relevant cohorts to provide real-world answers to medical questions. Among others, we probed the influence of treatment order: IO single (any BRAF status), IO → TKI, TKI → IO (see table). Preliminary results suggest that pts treated with TKI → IO had shorter OS despite balance prognostic factors to IO → TKI group. IO → TKI provides short-term benefits with 1.5-year OS similar to IO alone, but degraded 3-year OS. Conclusions: The PM approach enables fast and efficient visual inspection of the data, emphasizing treatment sequences over time and allowing stratification at different levels. More population attributes and outcomes will be included to further characterize treatment sequences and provide real-world insights into future personalized care options. [Table: see text]

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis-usefulness of density correction of volumetric CT data

The aim of this study was to apply density correction method to the quantitative image analysis of non-small cell lung cancer (NSCLC) computed tomography (CT) images, determining its influence on overall survival (OS) prediction of surgically treated patients. Clinicopathological (CP) data and preoperative CT scans, pre- and post-contrast medium (CM) administration, of 57 surgically treated NSCLC patients, were retrospectively collected. After CT volumetric density measurement of primary gross tumour volume (GTV), aorta and tracheal air, density correction was conducted on GTV (reference values: aortic blood and tracheal air). For each resulting data set (combining CM administration and normalization), first-order statistical and textural features were extracted. CP and imaging data were correlated with patients 1-, 3- and 5-year OS, alone and combined (uni-/multivariate logistic regression and Akaike information criterion). Predictive performance was evaluated using the ROC curves and AUC values and compared among non-normalized/normalized data sets (DeLong test). The best predictive values were obtained when combining CP and imaging parameters (AUC values: 1 year 0.72; 3 years 0.82; 5 years 0.78). After normalization resulted an improvement in predicting 1-year OS for some of the grey level size zonebased features (large zone low grey level emphasis) and for the combined CP-imaging model, a worse performance for grey level co-occurrence matrix (cluster prominence and shade) and first-order statistical (range) parameters for 1- and 5-year OS, respectively. The negative performance of cluster prominence in predicting 1-year OS was the only statistically significant result (p value 0.05). Density corrections of volumetric CT data showed an opposite influence on the performance of imaging quantitative features in predicting OS of surgically treated NSCLC patients, even if no statistically significant for almost all predictors.

Integrating radiomics into holomics for personalised oncology: from algorithms to bedside

Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other “omics” data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other “omics” into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called “holomics”, will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists.

Hypofractionated radiation therapy versus chemotherapy with temozolomide in patients affected by RPA class V and VI glioblastoma: a randomized phase II trial

INTRODUCTION

In RPA V-VI glioblastoma patients both hypofractionated radiotherapy and exclusive temozolomide can be used; the purpose of this trial is to compare these treatment regimens in terms of survival and quality of life.

METHODS

Patients with histologic diagnosis of glioblastoma were randomized to hypofractionated radiotherapy (RT-30 Gy in 6 fractions) and exclusive chemotherapy (CHT-emozolomide 200 mg/m²/day 5 days every 28 days). Overall (OS) and progression free survival (PFS) were evaluated with Kaplan Maier curves and correlated with prognostic factors. Quality- adjusted survival (QaS) was evaluated according to the Murray model (Neurological Sign and Symptoms-NSS) RESULTS: From 2010 to 2015, 31 pts were enrolled (CHT: 17 pts; RT: 14pts). Four pts were excluded from the analysis. RPA VI (p = 0.048) and absence of MGMT methylation (p = 0.001) worsened OS significantly. Biopsy (p = 0.048), RPA class VI (p = 0.04) and chemotherapy (p = 0.007) worsened PFS. In the two arms the initial NSS scores were overlapping (CHT: 12.23 and RT: 12.30) and progressively decreased in both group and became significantly worse after 5 months in CHT arm (p = 0.05). Median QaS was 104 days and was significantly better in RT arm (p = 0.01).

CONCLUSIONS

The data obtained are limited by the poor accrual. Both treatments were well tolerated. Patients in RT arm have a better PFS and QaS, without significant differences in OS. The deterioration of the NSS score would seem an important parameter and coincide with disease progression rather than with the toxicity of the treatment.

Towards a modular decision support system for radiomics: A case study on rectal cancer

Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics. A number of tools are available for supporting some of the steps of the radiomics process, but there is a lack of approaches which are able to deal with all the steps of the process. In this paper, we introduce a medical agent-based decision support system capable of handling the whole radiomics process. The proposed system is tested on two independent data sets of patients treated for rectal cancer. Experimental results indicate that the system is able to generate highly performant centre-specific predictive model, and show the issues related to differences in data sets collected by different centres, and how such issues can affect the performance of the generated predictive models.

Magnetic Resonance, Vendor-independent, Intensity Histogram Analysis Predicting Pathologic Complete Response After Radiochemotherapy of Rectal Cancer

PURPOSE

The objective of this study is finding an intensity based histogram (IBH) signature to predict pathologic complete response (pCR) probability using only pre-treatment magnetic resonance (MR) and validate it externally in order to create a workflow for the external validation of an MR IBH signature and to apply the model out of the environment where it has been tuned. The impact of pCR and the final predictors on the survival outcome were also evaluated.

METHODS AND MATERIALS

Three centers using different MR scanners were involved in this retrospective study. The first center recruited 162 patients for model training, and the second and third centers provided 34 plus 25 patients for external validation. Patients provided written consent. Accrual period was from May 2008 to December 2014. After surgery pathologic response was defined. T2-weighted MR scans acquired before chemoradiation therapy (CRT) were used for analysis addressed on primary lesions. Images were pre-processed using Laplacian of Gaussian (LoG) filter with multiple σ, and first order intensity histogram-based features (kurtosis, skewness, and entropy) were extracted. Features selection was performed using Mann-Whitney test. Tumor staging (cT, cN) was added to build a logistic regression model and predict pCR. Model performance was evaluated with internal and external validation using area under the curve (AUC) of the receiver operator characteristic (ROC) and calibration with Hosmer-Lemeshow test. The linear cross-correlation matrix (Pearson's coefficient) and the variance inflation factor (VIF) were used to check the correlation and the co-linearity among the final predictors. The amount of the information added through the radiomics features was estimated by using the DeLong's test, and the impact of pCR and the final predictors on survival outcomes were evaluated through the Kaplan-Meier curves by using the log-rank test and the multivariate Cox model.

RESULTS

Candidate-to-analysis features were skewness (σ = 0.485, P value = .01) and entropy (σ = 0.344, P value < .05). Logistic regression analysis showed as significant covariates cT (P value < .01), skewness-σ = 0.485 (P value = .01), and entropy-σ = 0.344 (P value < .05). Model AUCs were 0.73 (internal) and 0.75 (external).

CONCLUSIONS

This MR-based, vendor-independent model can be helpful for predicting pCR probability in locally advanced rectal cancer (LARC) patients only using pre-treatment imaging.

Assessing the conformity to clinical guidelines in oncology

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.

ENT COBRA ONTOLOGY: the covariates classification system proposed by the Head & Neck and Skin GEC-ESTRO Working Group for interdisciplinary standardized data collection in head and neck patient cohorts treated with interventional radiotherapy (brachytherapy)

PURPOSE

Clinical data collecting is expensive in terms of time and human resources. Data can be collected in different ways; therefore, performing multicentric research based on previously stored data is often difficult. The primary objective of the ENT COBRA (COnsortium for BRachytherapy data Analysis) ontology is to define a specific terminological system to standardized data collection for head and neck (H&N) cancer patients treated with interventional radiotherapy.

MATERIAL AND METHODS

ENT-COBRA is a consortium for standardized data collection for H&N patients treated with interventional radiotherapy. It is linked to H&N and Skin GEC-ESTRO Working Group and includes 11 centers from 6 countries. Its ontology was firstly defined by a multicentric working group, then evaluated by the consortium followed by a multi-professional technical commission involving a mathematician, an engineer, a physician with experience in data storage, a programmer, and a software expert.

RESULTS

Two hundred and forty variables were defined on 13 input forms. There are 3 levels, each offering a specific type of analysis: 1. Registry level (epidemiology analysis); 2. Procedures level (standard oncology analysis); 3. Research level (radiomics analysis). The ontology was approved by the consortium and technical commission; an ad-hoc software architecture ("broker") remaps the data present in already existing storage systems of the various centers according to the shared terminology system. The first data sharing was successfully performed using COBRA software and the ENT COBRA Ontology, automatically collecting data directly from 3 different hospital databases (Lübeck, Navarra, and Rome) in November 2017.

CONCLUSIONS

The COBRA Ontology is a good response to the multi-dimensional criticalities of data collection, retrieval, and usability. It allows to create a software for large multicentric databases with implementation of specific remapping functions wherever necessary. This approach is well-received by all involved parties, primarily because it does not change a single center's storing technologies, procedures, and habits.

Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer

The aim of this study was to propose a methodology to investigate the tumour heterogeneity and evaluate its ability to predict pathologically complete response (pCR) after chemo-radiotherapy (CRT) in locally advanced rectal cancer (LARC). This approach consisted in normalising the pixel intensities of the tumour and identifying the different sub-regions using an intensity-based thresholding. The spatial organisation of these subpopulations was quantified using the fractal dimension (FD). This approach was implemented in a radiomic workflow and applied to 198 T2-weighted pre-treatment magnetic resonance (MR) images of LARC patients. Three types of features were extracted from the gross tumour volume (GTV): morphological, statistical and fractal features. Feature selection was performed using the Wilcoxon test and a logistic regression model was calculated to predict the pCR probability after CRT. The model was elaborated considering the patients treated in two institutions: Fondazione Policlinico Universitario "Agostino Gemelli" of Rome (173 cases, training set) and University Medical Centre of Maastricht (25 cases, validation set). The results obtained showed that the fractal parameters of the subpopulations have the highest performance in predicting pCR. The predictive model elaborated had an area under the curve (AUC) equal to 0.77 ± 0.07. The model reliability was confirmed by the validation set (AUC = 0.79 ± 0.09). This study suggests that the fractal analysis can play an important role in radiomics, providing valuable information not only about the GTV structure, but also about its inner subpopulations.

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.

Prospective validation of pathologic complete response models in rectal cancer: Transferability and reproducibility

PURPOSE

Multiple models have been developed to predict pathologic complete response (pCR) in locally advanced rectal cancer patients. Unfortunately, validation of these models normally omit the implications of cohort differences on prediction model performance. In this work, we will perform a prospective validation of three pCR models, including information whether this validation will target transferability or reproducibility (cohort differences) of the given models.

METHODS

We applied a novel methodology, the cohort differences model, to predict whether a patient belongs to the training or to the validation cohort. If the cohort differences model performs well, it would suggest a large difference in cohort characteristics meaning we would validate the transferability of the model rather than reproducibility. We tested our method in a prospective validation of three existing models for pCR prediction in 154 patients.

RESULTS

Our results showed a large difference between training and validation cohort for one of the three tested models [Area under the Receiver Operating Curve (AUC) cohort differences model: 0.85], signaling the validation leans towards transferability. Two out of three models had a lower AUC for validation (0.66 and 0.58), one model showed a higher AUC in the validation cohort (0.70).

DISCUSSION

We have successfully applied a new methodology in the validation of three prediction models, which allows us to indicate if a validation targeted transferability (large differences between training/validation cohort) or reproducibility (small cohort differences).

PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

AIM

Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer.

MATERIALS & METHODS

The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org .

RESULTS

A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables.

CONCLUSION

The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.