Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia

Background: The quantitative morphological analysis of the trochlear region in the distal femur and the precise staging of the potential dysplastic condition constitute a key point for the use of personalized treatment options for the patella-femoral joint. In this paper, we integrated statistical shape models (SSM), able to represent the individual morphology of the trochlea by means of a set of parameters and stacked sparse autoencoder (SSPA) networks, which exploit the parameters to discriminate among different levels of abnormalities. Methods: Two datasets of distal femur reconstructions were obtained from CT scans, including pathologic and physiologic shapes. Both of them were processed to compute SSM of healthy and dysplastic trochlear regions. The parameters obtained by the 3D-3D reconstruction of a femur shape were fed into a trained SSPA classifier to automatically establish the membership to one of three clinical conditions, namely, healthy, mild dysplasia, and severe dysplasia of the trochlea. The validation was performed on a subset of the shapes not used in the construction of the SSM, by verifying the occurrence of a correct classification. Results: A major finding of the work is that SSM are able to represent anomalies of the trochlear geometry by means of specific eigenmodes of variation and to model the interplay between morphologic features related to dysplasia. Exploiting the patient-specific morphing parameters of SSM, computed by means of a 3D-3D reconstruction, SSPA is demonstrated to outperform traditional discriminant analysis in classifying healthy, mild, and severe trochlear dysplasia providing 99%, 97%, and 98% accuracy for each of the three classes, respectively (discriminant analysis accuracy: 85%, 89%, and 77%). Conclusions: From a clinical point of view, this paper contributes to support the increasing role of SSM, integrated with deep learning techniques, in diagnostics and therapy definition as quantitative and advanced visualization tools

Adaptive mathematical model of tumor response to radiotherapy based on CBCT data

Mathematical modeling of tumor response to radiotherapy has the potential of enhancing the quality of the treatment plan, which can be even tailored on an individual basis. Lack of extensive in vivo validation has prevented, however, reliable clinical translation of modeling outcomes. Image guided radiotherapy (IGRT) is a consolidated treatment modality based on computed tomographic (CT) imaging for tumor delineation and volumetric cone beam CT data for periodic checks during treatment. In this work, a macroscopic model of tumor growth and radiation response is proposed, being able to adapt along the treatment course as volumetric tumor data become available. Model parameter learning was based on cone beam CT images in 13 uterine cervical cancer patients, subdivided into three groups (G1, G2, G3) according to tumor type and treatment. Three groupspecific parameter sets (PS1, PS2 and PS3) on one general parameter set (PSa) were applied. The corresponding average model fitting errors were 14, 18, 13 and 21%, respectively. The model adaptation testing was performed using volume data of three patients, other than the ones involved in the parameter learning. The extrapolation performance of the general model was improved, while comparable prediction errors were found for the groupspecific approach. This suggests that an on-line parameter tuning can overcome the limitations of a suboptimal patient stratification, which appeared otherwise a critical issue

Model-supported radiotherapy personalization: In silico Test of Hyper- and Hypo-Fractionation Effects

The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected life and the economic efficiency of the treatment. In this paper, we propose a simulation tool that can be integrated into a decision support system that allows selection of the most suitable irradiation regimen. We used a macroscale mathematical model, which includes active and necrotic tumor dynamics and the role of oxygenation to simulate the effects of different hypo-/hyper-fractional regimens using retrospective data of seven virtual patients from as many cervical cancer patients used for its training in a previous study. The results confirmed the heterogeneous response across the patients as a function of treatment regimen and suggested the tumor growth rate as a main factor in the final tumor regression. In addition to the maximum regression, another criterion was suggested to select the most suitable regimen (minimum number of fractions to achieve a regression of 80%) minimizing the toxicity and maximizing the cost-effectiveness ratio. Despite the lack of direct validation, the simulation results are in agreement with the literature findings that suggest the need for hypo-fractionated regimens in case of aggressive tumor phenotypes. Finally, the paper suggests a possible exploitation of the model within a tool to support clinical decisions

Tumor radio-sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats

PURPOSE: Radiation therapy is one of the most common treatments in the fight against prostate cancer, since it is used to control the tumor (early stages), to slow its progression, and even to control pain (metastasis). Although many factors (e.g., tumor oxygenation) are known to influence treatment efficacy, radiotherapy doses and fractionation schedules are often prescribed according to the principle “one-fits-all,” with little personalization. Therefore, the authors aim at predicting the outcome of radiation therapy a priori starting from morphologic and functional information to move a step forward in the treatment customization. METHODS: The authors propose a two-step protocol to predict the effects of radiation therapy on individual basis. First, one macroscopic mathematical model of tumor evolution was trained on tumor volume progression, measured by caliper, of eighteen Dunning R3327-AT1 bearing rats. Nine rats inhaled 100% O(2) during irradiation (oxy), while the others were allowed to breathe air. Second, a supervised learning of the weight and biases of two feedforward neural networks was performed to predict the radio-sensitivity (target) from the initial volume and oxygenation-related information (inputs) for each rat group (air and oxygen breathing). To this purpose, four MRI-based indices related to blood and tissue oxygenation were computed, namely, the variation of signal intensity [Formula: see text] in interleaved blood oxygen level dependent and tissue oxygen level dependent (IBT) sequences as well as changes in longitudinal [Formula: see text] and transverse [Formula: see text] relaxation rates. RESULTS: An inverse correlation of the radio-sensitivity parameter, assessed by the model, was found with respect the [Formula: see text] (−0.65) for the oxy group. A further subdivision according to positive and negative values of [Formula: see text] showed a larger average radio-sensitivity for the oxy rats with [Formula: see text] and a significant difference in the two distributions (p < 0.05). Finally, a leave-one-out procedure yielded a radio-sensitivity error lower than 20% in both neural networks. CONCLUSIONS: While preliminary, these specific results suggest that subjects affected by the same pathology can benefit differently from the same irradiation modalities and support the usefulness of IBT in discriminating between different responses

A Multiparameter Approach to Evaluate Post-Stroke Patients: An Application on Robotic Rehabilitation

Multidomain instrumental evaluation of post-stroke chronic patients, coupled with standard clinical assessments, has rarely been exploited in the literature. Such an approach may be valuable to provide comprehensive insight regarding patients&#8217; status, as well as orienting the rehabilitation therapies. Therefore, we propose a multidomain analysis including clinically compliant methods as electroencephalography (EEG), electromyography (EMG), kinematics, and clinical scales. The framework of upper-limb robot-assisted rehabilitation is selected as a challenging and promising scenario to test the multi-parameter evaluation, with the aim to assess whether and in which domains modifications may take place. Instrumental recordings and clinical scales were administered before and after a month of intensive robotic therapy of the impaired upper limb, on five post-stroke chronic hemiparetic patients. After therapy, all patients showed clinical improvement and presented pre/post modifications in one or several of the other domains as well. All patients performed the motor task in a smoother way; two of them appeared to change their muscle synergies activation strategies, and most subjects showed variations in their brain activity, both in the ipsi- and contralateral hemispheres. Changes highlighted by the new multiparametric instrumental approach suggest a recovery trend in agreement with clinical scales. In addition, by jointly demonstrating lateralization of brain activations, changes in muscle recruitment and the execution of smoother trajectories, the new approach may help distinguish between true functional recovery and the adoption of suboptimal compensatory strategies. In the light of these premises, the multi-domain approach may allow a finer patient characterization, providing a deeper insight into the mechanisms underlying the relearning procedure and the level (neuro/muscular) at which it occurred, at a relatively low expenditure. The role of this quantitative description in defining a personalized treatment strategy is of great interest and should be addressed in future studies

Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging

This article describes a macroscopic mathematical modeling approach to capture the interplay between solid tumor evolution and cell damage during radiotherapy. Volume regression profiles of 15 patients with uterine cervical cancer were reconstructed from serial cone-beam computed tomography data sets, acquired for image-guided radiotherapy, and used for model parameter learning by means of a genetic-based optimization. Patients, diagnosed with either squamous cell carcinoma or adenocarcinoma, underwent different treatment modalities (image-guided radiotherapy and image-guided chemo-radiotherapy). The mean volume at the beginning of radiotherapy and the end of radiotherapy was on average 23.7 cm(3) (range: 12.7-44.4 cm(3)) and 8.6 cm(3) (range: 3.6-17.1 cm(3)), respectively. Two different tumor dynamics were taken into account in the model: the viable (active) and the necrotic cancer cells. However, according to the results of a preliminary volume regression analysis, we assumed a short dead cell resolving time and the model was simplified to the active tumor volume. Model learning was performed both on the complete patient cohort (cohort-based model learning) and on each single patient (patient-specific model learning). The fitting results (mean error: ∼16% and ∼6% for the cohort-based model and patient-specific model, respectively) highlighted the model ability to quantitatively reproduce tumor regression. Volume prediction errors of about 18% on average were obtained using cohort-based model computed on all but 1 patient at a time (leave-one-out technique). Finally, a sensitivity analysis was performed and the data uncertainty effects evaluated by simulating an average volume perturbation of about 1.5 cm(3) obtaining an error increase within 0.2%. In conclusion, we showed that simple time-continuous models can represent tumor regression curves both on a patient cohort and patient-specific basis; this discloses the opportunity in future to exploit such models to predict how changes in the treatment schedule (number of fractions, doses, intervals among fractions) might affect the tumor regression on an individual basis

Modeling the interplay between tumor volume regression and oxygenation in uterine cervical cancer during radiotherapy treatment

This paper describes a patient-specific mathematical model to predict the evolution of uterine cervical tumors at a macroscopic scale, during fractionated external radiotherapy. The model provides estimates of tumor re-growth and dead-cell reabsorption, incorporating the interplay between tumor regression rate and radiosensitivity, as a function of the tumor oxygenation level. Model parameters were estimated by minimizing the difference between predicted and measured tumor volumes, these latter being obtained from a set of 154 serial cone-beam computed tomography (CBCT) scans acquired on 16 patients along the course of the therapy. The model stratified patients according to two different estimated dynamics of dead-cell removal and to the predicted initial value of the tumor oxygenation. The comparison with a simpler model demonstrated an improvement in fitting properties of this approach (fitting error average value <5%, p<0.01), especially in case of tumor late responses, which can hardly be handled by models entailing a constant radiosensitivity, failing to model changes from initial severe hypoxia to aerobic conditions during the treatment course. The model predictive capabilities suggest the need of clustering patients accounting for cancer cell-line, tumor staging, as well as microenvironment conditions (e.g. oxygenation level)