Siemens Medical Solutions Usa, Inc.

Stichting Maastricht Radiation Oncology “Maastro-Clinic”

H. Lee Moffitt Cancer Center and Research Institute, Inc.

Universiteit Maastricht

Other

Leland Stanford Junior University

Université Montpellier 2 Sciences Et Techniques

Academisch Ziekenhuis Maastricht

Philippe Lambin

Sriram Krishnan

Glenn Fung

R Bharat Rao

Shipeng Yu

Cary Dehing-Oberije

Dirk De Ruysscher

Sandy A Napel

Robert J Gillies

Steven A Eschrich

Andreas Lubbertus Aloysius Johannes Dekker

Daniel L Rubin

Sylvia K Plevritis

Robert A Gatenby

Andreas L A J Dekker

Balaji Krishnapuram

Claudiu Supuran

Harald Steck

Bradly G Wouters

Jean-Yves Winum

Christoffel Theodorus Anthonius Evelo

Steven Florian Petit

Adrian Campbell Begg

Hugo J W L Aerts

Sebastian Sanduleanu

Claudia Offermann

Michel Oellers

Sara Joao Botelho De Carvalho

Kasper M Rouschop

Kartik Jayasurya Komati

Ralph Theodoor Hubertina Leijenaar

Renaud G Seigneuric

Ludwig Jerome Dubois

Maud H W Starmans

Hugo Johannes Wilhelmus Louis Aerts

Dimitry S A Nuyten

Arie Van Erk

Michael Gaston Magagnin

An Mieke Voets

Sara João Botelho De Carvalho

Georgi Ilkov Nalbantov

Hubertus Julius Maria Smeets

The present document relates to providing a radiation treatment plan for treatment of a neoplasm, including the steps of: obtaining an image including the neoplasm and obtaining first segmentation data for segmenting at least one target-volume to be targeted with radiation. Further identifying any organs-at-risk and segmenting these. The method further comprises identifying lymphocyte-rich-organs in the image, and obtaining third segmentation data for segmenting the lymphocyte-rich-organs. The planning system then obtains radiation dose regime data, including first, second and third dose regime data. The planning system then determines a radiation treatment plan which provides treatment process parameters for operating one or more radiation beams for radiation treatment of the neoplasm, The process parameters are determined to apply the radiation at a first radiation dose to the target volume which corresponds with the first dose regime data, apply the radiation at a minimized second radiation dose to the or each organs-at-risk, and apply the radiation at a third radiation dose to the or each lymphocyte-rich-organ which corresponds with the third dose regime data, wherein the third radiation dose amount is minimized.

Treatment and planning for lymphocytes sparing radiotherapy

The present document describes a training method of a machine learning data processing model for determining a hypoxia status of a neoplasm, in particular a random forest model. The method comprises obtaining, for a plurality of neoplasms, at least one data sample comprising 3D imaging data. A hypoxic volume fraction is determined for each data sample, as well as a set of image features associated with the neoplasm. The method further iterates a sequence of training steps and each iteration includes: selecting a subset of image features and eliminating, for each data sample, the subset of image features to yield a reduced set of image features. The iteration also includes generating decision trees, providing a momentary random forest model based thereon, and submitting a test set of image features to the momentary random forest model to yield a performance value. The iterations are continued until all image features have been selected for a subset at least once, and then a plurality of preferred image features are selected for providing a radiomics feature signature. The trained random forest data processing model based on decision trees associated with the preferred image features of the radiomics feature signature.

Method of training a machine learning data processing model, method of determining a hypoxia status of a neoplasm in a human or animal body, and system therefore

An example method for analyzing quantitative information obtained from radiological images includes identifying a ROI or a VOI in a radiological image, segmenting the ROI or the VOI from the radiological image and extracting quantitative features that describe the ROI or the VOI. The method also includes creating a radiological image record including the quantitative features, imaging parameters of the radiological image and clinical parameters and storing the radiological image record in a data structure containing a plurality of radiological image records. In addition, the method includes receiving a request with the patient's radiological image or information related thereto, analyzing the data structure to determine a statistical relationship between the request and the radiological image records and generating a patient report with a diagnosis, a prognosis or a recommended treatment regimen for the patient's disease based on a result of analyzing the data structure.

Systems, methods and devices for analyzing quantitative information obtained from radiological images

The present invention relates to an image analysis method for providing information for supporting illness development prediction regarding a neoplasm in a human or animal body. The method includes receiving for the neoplasm first and second image data at a first and second moment in time, and deriving for a plurality of image features a first and a second image feature parameter value from the first and second image data. These feature parameter values being a quantitative representation of a respective image feature. Further, calculating an image feature difference value by calculating a difference between the first and second image feature parameter value, and based on a prediction model deriving a predictive value associated with the neoplasm for supporting treatment thereof. The prediction model includes a plurality of multiplier values associated with image features. For calculating the predictive value the method includes multiplying each image feature difference value with its associated multiplier value and combining the multiplied image feature difference values.

Image analysis method supporting illness development prediction for a neoplasm in a human or animal body

The present invention relates to a decision support system and an image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, comprising the steps of: receiving, by a processing unit, image data of the neoplasm; and deriving, by the processing unit, a plurality of image feature parameter values from the image data, said image parameter values relating to image features associated with the neoplasm; and deriving, by said processing unit using a signature model, one or more neoplasm signature model values associated with the neoplasm from said image feature parameter values, wherein said signature model includes a functional relation between or characteristic values of said image feature parameter values for deriving said neoplasm signature model values.

Method and system for determining a phenotype of a neoplasm in a human or animal body

The present invention concerns novel carbonic anhydrase IX inhibitors comprising a nitroimidazole moiety and their use in therapy of hypoxic conditions, in particular cancer treatment, especially chemotherapy and radiotherapy. The compounds of the invention have an increased specificity for the carbonic anhydrase IX enzyme compared to the art. The present invention relates to novel nitroimidazole derivates represented by formula (1). 

Cancer targeting using carbonic anhydrase isoform IX inhibitors

Functional imaging information is used to determine a probability of residual disease given a treatment. The functional imaging information shows different characteristic levels for different regions of the tumor. The probability is output for planning use and/or used to automatically determine dose by region. Using the probability, the dose may be distributed by region so that some regions receive a greater dose than other regions. This distribution by region of dose more likely treats the tumor with a same dose, allows a lesser dose to sufficient treat the tumor, and/or allows a greater dose with a lesser or no increase in risk to normal tissue. The dose plan may account for personalized tumors as each patient may have distinct tumors. Probability of dose application accuracy may also be used, so that a combined treatment probability allows efficient dose planning.

Dose distribution modeling by region from functional imaging

A computer-implemented method for privacy-preserving data mining to determine cancer survival rates includes providing a random matrix B agreed to by a plurality of entities, wherein each entity i possesses a data matrix Aof cancer survival data that is not publicly available, providing a class matrix Dfor each of the data matrices A, providing a kernel K(A, B) by each of said plurality of entities to allow public computation of a full kernel, and computing a binary classifier that incorporates said public full kernel, wherein said classifier is adapted to classify a new data vector according to a sign of said classifier.

System and method for privacy preserving predictive models for lung cancer survival analysis

Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding.

Knowledge-based interpretable predictive model for survival analysis

Modeling of prognosis of survivability, side-effect, or both is provided. For example, RILI is predicted using bullae information. The amount, volume or ratio of Bullae, even alone, may indicate the likelihood of complication, such as the likelihood of significant (e.g., stage 3) pneumonitis. As another example, RILI is predicted using uptake values of an imaging agent. Standardized uptake from a functional image (e.g., FDG uptake from a positron emission image), alone or in combination with other features, may indicate the likelihood of side-effect. In another example, survivability, such as two-year survivability, is predicted using blood biomarkers. The characteristics of a patient's blood may be measured and, alone or in combination with other features, may indicate the likelihood of survival. The modeling may be for survivability, side-effect, or both and may use one or more of the blood biomarker, uptake value, and bullae features.

Modeling lung cancer survival probability after or side-effects from therapy

The present invention provides methods and compositions for predicting patient responses to cancer treatment using hypoxia gene signatures. These methods can comprise measuring in a biological sample from a patient the levels of gene expression of a group of the genes designated herein. The present invention also provides for microarrays that can detect expression from a group of genes.

Gene signature of early hypoxia to predict patient survival

A predictor of medical treatment outcome is developed and applied. A prognosis model is developed from literature. The model is determined by reverse engineering the literature reported quantities. A relationship of a given variable to a treatment outcome is derived from the literature. A processor may then use individual patient values for one or more variables to predict outcome. The accuracy may be increased by including a data driven model in combination with the literature driven model.

Prognosis modeling from literature and other sources

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Genappe-Bousval, Belgium

Philippe Lambin