C. Masciocchi

Time to surgery and pathologic complete response after neoadjuvant chemoradiation in rectal cancer: A population study on 2094 patients

Highlights • A large population based analysis to evaluate pathologic response according to time of surgery.• LARC patients were treated with modern techniques of radiotherapy and surgery.• The rate of pCR increased according to time interval from 12.6% to 31.1%.• The pCR increasing was 1.5% (about 0.2%/die) per each week of waiting.• Lengthening the interval (>13 weeks) significantly improved the pathological response.

Integrating Downstaging in the Risk Assessment of Patients With Locally Advanced Rectal Cancer Treated With Neoadjuvant Chemoradiotherapy: Validation of Valentini's Nomograms and the Neoadjuvant Rectal Score

Background Adjuvant chemotherapy is controversial in patients with locally advanced rectal cancer after preoperative chemoradiation. Valentini et al developed 3 nomograms (VN) to predict outcomes in these patients. The neoadjuvant rectal score (NAR) was developed after VN to predict survival. We aimed to validate these tools in a retrospective cohort at an academic institution. Patients and Methods VN and the NAR were applied to 158 consecutive patients with locally advanced rectal cancer treated with chemoradiation followed by surgery. According to the score, they were divided into low, intermediate, or high risk of relapse or death. For statistical analysis, we performed Kaplan‐Meier curves, log‐rank tests, and Cox regression analysis. Results Five‐year overall survival was 83%, 77%, and 67% for low‐, intermediate‐, and high‐risk groups, respectively (P = .023), according to VN, and 84%, 71%, and 59% for low‐, intermediate‐, and high‐risk groups, respectively (P = .004), according to NAR. When the score was considered as a continuous variable, a significant association with the risk of death was observed (NAR: hazard ratio, 1.04; P < .001; VN: hazard ratio, 1.10; P < .001). Conclusion We confirmed the value of these scores to stratify patients according to their individual risk when designing new trials. Micro‐Abstract Valentini’s nomograms and the neoadjuvant rectal score are useful tools that integrate different prognostic variables, providing an estimation of the risk of relapse or death in patients with resected rectal cancer treated via a neoadjuvant approach. We performed a retrospective validation in a cohort from a single institution that confirmed their prognostic value.

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.

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.

Nomogram for predicting radiation maculopathy in patients treated with Ruthenium-106 plaque brachytherapy for uveal melanoma

Purpose To develop a predictive model and nomogram for maculopathy occurrence at 3 years after 106Ru/106Rh plaque brachytherapy in uveal melanoma. Material and methods Clinical records of patients affected by choroidal melanoma and treated with 106Ru/106Rh plaque from December 2006 to December 2014 were retrospectively reviewed. Inclusion criteria were: dome-shaped melanoma, distance to the fovea > 1.5 mm, tumor thickness > 2 mm, and follow-up > 4 months. The delivered dose to the tumor apex was 100 Gy. Primary endpoint of this investigation was the occurrence of radiation maculopathy at 3 years. Analyzed factors were as follows: gender, age, diabetes, tumor size (volume, area, largest basal diameter and apical height), type of plaque, distance to the fovea, presence of exudative detachment, drusen, orange pigment, radiation dose to the fovea and sclera. Univariate and multivariate Cox proportional hazards analyses were used to define the impact of baseline patient factors on the occurrence of maculopathy. Kaplan-Meier curves were used to estimate freedom from the occurrence of the maculopathy. The model performance was evaluated through internal validation using area under the ROC curve (AUC), and calibration with Gronnesby and Borgan tests. Results One hundred ninety-seven patients were considered for the final analysis. Radiation-related maculopathy at 3 years was observed in 41 patients. The proposed nomogram can predict maculopathy at 3 years with an AUC of 0.75. Distance to fovea appeared to be the main prognostic factor of the predictive model (hazard ratio of 0.83 [0.76-0.90], p < 0.01). Diabetes (hazard radio of 2.92 [1.38-6.20], p < 0.01), and tumor volume (hazard radio of 21.6 [1.66-281.14], p = 0.02) were significantly predictive for maculopathy occurrence. The calibration showed no statistical difference between actual and predicted maculopathy (p = 1). Conclusions Our predictive model, together with its nomogram, could be a useful tool to predict the occurrence of radiation maculopathy at 3 years after the treatment.

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.