Patent Description:
Radiotherapy uses charged particles, e.g., electrons or ions, to treat cancer. It is also possible to use photons. The particles deposit energy at a tumor to physically destroy the tumor.

Radiotherapy has been a useful treatment for many types of cancer. Nonetheless, it has been observed that some patients subsequently experience radiation-induced toxicity. This is explained in further detail hereinafter.

<CIT> discloses a method, a system and a computer-readable media for treatment plan risk analysis. Non-patent literature - <NPL>. - discloses a xerostomia prediction model with radiation treatment data using a <NUM>-dimensional (3D) residual convolutional neural network. <CIT> discloses a method for constructing a rectal toxicity prediction system.

Non-patent literature - <NPL>. - discloses a convolutional neural network model to analyze rectum dose distribution and predict rectum toxicity.

One example employs radiotherapy to treat non-small cell lung cancer (NSCLC). Stereotactic body radiation therapy (SBRT) is the standard of care for medically inoperable patients with early-stage NSCLC. However, the lung is a radiosensitive organ and radiation pneumonitis - as an example of radiation-induced toxicity - can occur after exposure to radiation of larger than <NUM> Gray in only a few months. Lung pneumonitis is manifested by loss of epithelial cells, edema, inflammation, fibrosis, and occlusion of airways, blood vessels, and sacs. The vulnerability of patients to be subject to radiation pneumonitis is directly correlated with any underlying preexisting disease of the lungs. Additionally, standard approaches, such as SBRT, for radiotherapy that demonstrate efficacy for a population may not achieve optimal results for individual patients.

Consequently, it is difficult to individualize current standard radiotherapy treatments, such as SBRT for treating NSCLC, based on anatomical and physiological characteristics of individual patients; therefore, it is difficult to select a radiotherapy treatment that is most likely to be adapted to an individual patient to maximize the likelihood of treatment response while minimizing the risk of adverse effects, such as radiation-induced toxicity.

Therefore, there is a need for advanced techniques of planning a radiotherapy treatment. In particular, there is a need for advanced techniques of predicting - prior to a radiotherapy treatment - a patient's vulnerability to radiation-induced toxicity.

A method for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient is disclosed. The method comprises receiving data associated with a region of interest comprising the target region. The received data comprises a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. The method further comprises applying a trained machine-learning algorithm to the received data, and generating, by the trained machine-learning algorithm, at least one toxicity indicator based on the received data. The at least one toxicity indicator is indicative of the risks of the radiation-induced toxicity. The trained machine-learning algorithm is trained using the method of performing a training of a machine-learning algorithm described below.

A method of performing a training of a machine-learning algorithm for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient is disclosed. The method comprises receiving multiple instances of training data associated with a region of interest comprising the target region and multiple instances of reference data. Each one of the multiple instances of the reference data corresponds to a respective instance of the training data. Each one of the multiple instances of the training data comprises a dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. Each instance of the multiple instances of the reference data comprises at least one diagnosed toxicity indicator indicative of diagnosed risks of radiation-induced toxicity. The method further comprises processing the multiple instances of the training data by the machine-learning algorithm, and generating, by the machine-learning algorithm and for each one of the multiple instances of the training data, at least one respective estimated toxicity indicator indicative of estimated risks of radiation-induced toxicity. The method further comprises performing the training of the machine-learning algorithm by updating parameter values of the machine-learning algorithm based on a comparison between the diagnosed toxicity indicators and corresponding estimated toxicity indicators.

A device comprises a processing unit, a memory unit and an input/output interface. The processing unit is configured to execute a program stored in the memory unit to perform a method for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient. The method comprises receiving data associated with a region of interest comprising the target region. The received data comprises a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. The method further comprises applying a trained machine-learning algorithm to the received data, and generating, by the trained machine-learning algorithm, at least one toxicity indicator based on the received data. The at least one toxicity indicator is indicative of the risks of the radiation-induced toxicity.

A device comprises a processing unit, a memory unit and an input/output interface. The processing unit is configured to execute a program stored in the memory unit to perform a method of performing a training of a machine-learning algorithm for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient. The method comprises receiving multiple instances of training data associated with a region of interest comprising the target region and multiple instances of reference data. Each one of the multiple instances of the reference data corresponds to a respective instance of the training data. Each one of the multiple instances of the training data comprises a dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. Each instance of the multiple instances of the reference data comprises at least one diagnosed toxicity indicator indicative of diagnosed risks of radiation-induced toxicity. The method further comprises processing the multiple instances of the training data by the machine-learning algorithm, and generating, by the machine-learning algorithm and for each one of the multiple instances of the training data, at least one respective estimated toxicity indicator indicative of estimated risks of radiation-induced toxicity. The method additionally comprises performing the training of the machine-learning algorithm by updating parameter values of the machine-learning algorithm based on a comparison between the diagnosed toxicity indicators and corresponding estimated toxicity indicators.

A computer program product or a computer program or a computer-readable storage medium includes program code. The program code can be executed by at least one processor. Executing the program code causes the at least one processor to perform a method for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient. The method comprises receiving data associated with a region of interest comprising the target region. The received data comprises a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. The method further comprises applying a trained machine-learning algorithm to the received data, and generating, by the trained machine-learning algorithm, at least one toxicity indicator based on the received data. The at least one toxicity indicator is indicative of the risks of the radiation-induced toxicity.

A computer program product or a computer program or a computer-readable storage medium includes program code. The program code can be executed by at least one processor. Executing the program code causes the at least one processor to perform a method of performing a training of a machine-learning algorithm for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient. The method comprises receiving multiple instances of training data associated with a region of interest comprising the target region and multiple instances of reference data. Each one of the multiple instances of the reference data corresponds to a respective instance of the training data. Each one of the multiple instances of the training data comprises a dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. Each instance of the multiple instances of the reference data comprises at least one diagnosed toxicity indicator indicative of diagnosed risks of radiation-induced toxicity. The method further comprises processing the multiple instances of the training data by the machine-learning algorithm, and generating, by the machine-learning algorithm and for each one of the multiple instances of the training data, at least one respective estimated toxicity indicator indicative of estimated risks of radiation-induced toxicity. The method additionally comprises performing the training of the machine-learning algorithm by updating parameter values of the machine-learning algorithm based on a comparison between the diagnosed toxicity indicators and corresponding estimated toxicity indicators.

The drawings are to be regarded as being schematic representations and elements illustrated in the drawings, which are not necessarily shown to scale.

Various examples described herein generally relate to techniques of planning a radiotherapy treatment. The radiotherapy treatment can rely on irradiating a tumor to destroy cancer cells. It is possible to use charged particles such as ions or electrons, or even high-energy photons.

Thus, in other words, radiotherapy (also called radiation therapy) is a cancer treatment that uses high doses of radiation to kill cancer cells and shrink tumors. At high doses, radiation therapy kills cancer cells or slows their growth by damaging their deoxyribonucleic acid (DNA). Cancer cells whose DNA is damaged beyond repair stop dividing or die. When the damaged cells die, they are broken down and removed by the body. Radiation therapy does not necessarily kill cancer cells right away. It takes days or weeks of treatment before DNA is damaged enough for cancer cells to die. Then, cancer cells keep dying for weeks or months after radiation therapy ends.

As a general rule, there are two main types of radiation therapy, external beam radiation therapy and internal radiation therapy. External beam radiation therapy is generated by a machine that aims the radiation at a target region. The incident path of the radiation is adjustable by relatively positioning the machine with respect to the patient. On the other hand, internal radiation therapy is a treatment in which a source of radiation is put inside the patient's body. The radiation source can be solid or liquid, which are called brachytherapy and systemic therapy, respectively.

Various techniques described herein generally relate to predicting risks of radiation-induced toxicity associated with the radiotherapy treatment. More specifically, at least one toxicity indicator indicative of the risks of the radiation-induced toxicity can be determined. Thereby, a prediction of a risk of adverse side-effects of the radiotherapy treatment can be made.

Various options are generally available for implementing the at least one toxicity indicator. Some examples are summarized in TAB. <NUM> below.

It would be possible to use the at least one toxicity indicator to adjust a treatment plan of the radiotherapy treatment. Various options are available for adjusting the treatment plan. A dose map (cf. <NUM>, example I) could be adjusted. For example, it would be possible to decrease a dose that is administered by the radiotherapy treatment. It would be possible to adjust an energy of the particles. It would be possible to change and impact path of the particles. Such tasks could be computer-implemented using a respective algorithm.

According to various examples, it is possible to determine the at least one toxicity indicator by applying a trained machine-learning algorithm, such as a deep neural network, to input data.

According to the various examples described herein, various options are available for implementing the input data. Some examples are listed in TAB.

<FIG> schematically illustrates a radiation source for providing radiation for a radiotherapy treatment. This serves as an example of a hardware implementation of a medical tool used for radiation therapy. Today medical linear accelerators (referred to as linacs) account for most of the operational megavoltage radiotherapy treatment units in clinical use. <FIG> is a block diagram of a high-energy bent-beam medical linear accelerator <NUM> showing the major components. The linac <NUM> uses microwave pulses <NUM>, e.g., having frequencies in the S-band microwave region (e.g., <NUM>,<NUM>,) to generate an electric field. The microwave radiation of the microwave pulse <NUM> is propagated through an accelerator waveguide <NUM> and electrons which are generated by an electron gun <NUM> and used to create an X-ray - i.e., photon - (or electron) beam for treatment <NUM> are injected into the accelerator waveguide <NUM> and accelerated by the generated electric field. The structure of the accelerator waveguide <NUM> comprises a stack of cylindrical metal cavities having an axial hole <NUM> through which the accelerated electrons pass. The linac <NUM> further comprises a modulator <NUM> which converts a line AC (analog current) power <NUM> to an RF (radio frequency) power source <NUM> needed for the production of the microwave pulse <NUM>, and a high voltage pulse <NUM> supplied to the electron gun <NUM>.

Once accelerated, the accelerated high-energy electrons emerge at the axial hole <NUM> and are directed to strike a metal target (not shown in <FIG>). This abrupt stopping of the electrons results in the conversion of their kinetic energy partially to heat in the metal target and partially to the production of bremsstrahlung X-rays. Because the accelerator waveguide <NUM> is typically placed horizontally or at some angle with respect to the horizontal, the electrons are bent by a bending system <NUM> and through a suitable angle, usually <NUM> or <NUM> degrees between the accelerator waveguide <NUM> and the X-ray target. In addition, the linac <NUM> also comprises a dosimetry system <NUM> and/or collimation system <NUM> for adjusting radiation doses. The dosimetry system <NUM> and/or the collimation system <NUM> can be controlled by a computing device, such as a computer or a server (not shown in <FIG>) directly connected to the linac <NUM>, a server or a computer remotely connected to the linac <NUM> via a network.

The linac <NUM> can operate based on a treatment plan that specifies its relative positioning to the patient and the energy of the emitted particles in each position. The duration of the emission can be specified. The treatment plan thus defines a dose map (cf. <NUM>, example I).

The linac <NUM> of <FIG> is one example of hardware used for performing a radiation therapy treatment. As a general rule, radiotherapy treatments mentioned in this disclosure may be performed by any one of the following radiotherapy treatment systems: an intensity modulated radiotherapy (IMRT) system, an image-guided radiotherapy (IGRT) system, an intensity modulated art therapy (IMAT) system, a rotational IMRT system, a Tomotherapy IMRT, a CyberKnife® system, a Gamma Knife system, a <NUM>-D IGRT system, a proton beam radiation therapy (PBRT) system, a light ion radiation therapy system, a heavy ion radiation therapy system.

<FIG> schematically illustrates a target region <NUM> comprising a tumor <NUM> and a region of interest (ROI) <NUM> comprising the target region <NUM>. The ROI <NUM> can include or be part of a specific organ, such as a lung, a brain, a liver, a pancreas, a kidney and so on. Images of ROI <NUM> may be obtained from imaging data <NUM> of a patient acquired by using at least one medicine imaging scanning techniques, such as radiographic imaging comprising CT (computed tomography) and X-ray, nuclear medicine imaging comprising positron emission tomography (PET) and single-photon emission computerized tomography (SPECT), MRI (magnetic resonance imaging) scans, ultrasound imaging and etc. (cf. <NUM>, example II).

Within the ROI <NUM>, there are healthy/normal tissues/cells <NUM> surrounding the target region <NUM>. During the radiotherapy treatment, both tumor cells and healthy cells within the target region <NUM> as well as healthy cells closely surrounding the target region <NUM> are irradiated by radiation - emitted by, e.g., the linac <NUM> according to <FIG>. In a period of time after the radiotherapy treatment, the healthy cells within the ROI <NUM> may suffer from radiation-induced damages or diseases, e.g., radiation-induced pneumonitis.

A radiation oncologist, when planning a radiotherapy treatment for a patient with cancer, determines a treatment plan a radiation dose that is large enough to potentially cure or control the disease within the target region <NUM>, but does not cause serious healthy/normal tissue complications, such as radiation-induced toxicity. Various examples are based on the finding that this task can be challenging, because tumor control and healthy/normal tissue effect responses are typically steep functions of radiation dose; that is, a small change in the dose delivered (±<NUM>%) can result in a change in the local response of the tissue (±<NUM>%). Moreover, the prescribed curative doses are often, by necessity, very close to the doses tolerated by the healthy/normal tissues. Because of this small "therapeutic window" for optimum treatment, the radiation dose must be planned and delivered with a high degree of accuracy to avoid serious normal tissue complications, such as radiation-induced toxicity.

According to various examples, an individual patient can be assigned a score (cf. <NUM>) - i.e., the at least one toxicity indicator - indicating his/her level of vulnerability to radiation-induced toxicity, then this information can be used to modify the treatment plan, e.g., to thereby obtain a modified dose map.

Thus, according to the techniques described herein a predictive method is provided which, based on various pre-treatment diagnostic or planning tests such as radiographic imaging scans, nuclear medicine imaging scans, MRI scans, ultrasound imaging scans and other clinical data such as EEG (electroencephalogram), ECG (electrocardiogram), can determine the at least one radiation-induced toxicity indicator as a risk score quantifying the toxicity level the patient could potentially experience after the therapy.

Hereinafter, techniques of machine-learning, particularly deep-learning, for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient are described. Supervised learning relying on labeled training data can be used to parameterize a machine-learning algorithm. In supervised learning, a target variable is known as ground truth, and a machine-learning algorithm can learn the pattern between dependent and independent variables.

First - in connection with <FIG> - details with respect to the inference of at least one toxicity indicator are described, based on a pre-trained machine-learning algorithm.

The training will be later-on explained in connection with <FIG>.

<FIG> is a flowchart of a method <NUM> according to various examples. Optional blocks are labeled with dashed lines. For example, the method <NUM> according to <FIG> may be executed by a computing device directly or remotely connected to the linac <NUM> according to the example of <FIG> or another radiation-therapy hardware, e.g., upon loading program code from a memory. It would also be possible that the method <NUM> is at least partially executed by a separate compute unit, e.g. at a server backend or using cloud computing. The computing device or the separate compute unit may comprise at least one circuit - e.g., a CPU and/or a GPU and/or a TPU - for executing the method <NUM>.

The method <NUM> can predict risks of radiation-induced toxicity based on pre-treatment data capturing underlying and subtle signatures of healthy tissues surrounding a tumor, e.g., lung parenchyma, vulnerability to radiation. Such information may be combined with planning medicine imaging scans as mentioned-above and a planned dose map capturing both the tumor characteristics and potential dosimetric level and its distribution over not only the tumor but the surrounding presumably healthy tissues, e.g., lung parenchyma.

In detail, at block <NUM>, the computing device or the separate compute unit receives data associated with a ROI (e.g., ROI <NUM> according to <FIG>) comprising the target region <NUM>. The data may be received from a database or a clinical planning system. The data may be received from a memory.

At block <NUM>, input data according to TAB. <NUM> may be used. Specifically, the received data may comprise a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the ROI <NUM>.

For example, pre-radiotherapy-treatment imaging data of the ROI <NUM> may be used (cf. <NUM>, option II). This imaging data may comprise at least one of X-ray imaging data, CT imaging data, MRI imaging data, PET imaging data, SPECT imaging data, ultrasound imaging data. If the pre-radiotherapy-treatment imaging data comprises two and more imaging data of different imaging modalities, registration may be applied to the pre-radiotherapy-treatment imaging data, e.g., registering CT imaging data with MRI imaging data, and/or PET imaging data. Further, the pre-radiotherapy-treatment imaging data may be registered with the predefined dose map.

The received data may further comprise at least one of a dose level (e.g., <NUM> Gy) of the radiotherapy treatment, a clinical stage of a tumor located in the target region, comorbidities of the patient, and/or demographics of the patient, as previously explained in connection with TAB.

At block <NUM>, the computing device or the separate compute unit applies a trained machine-learning algorithm to the received data.

As a general rule, according to the various examples described herein, the trained machine-learning algorithm may be a (deep) neural network, e.g., a convolutional neural network, a recurrent neural network, a generative adversarial network, a residual network and etc. For example, the trained machine-learning algorithm comprises an encoder for extracting pertinent features (sometimes also referred to as latent features) of the ROI based on the received data and a classifier for generating the at least one toxicity indicator based on the extracted pertinent features of the ROI.

Further, the trained machine-learning algorithm may be obtained by utilizing supervised learning, semi-supervised learning, or reinforcement learning. Preferably, the trained machine-learning algorithm is trained by using supervised learning. A computer-implemented method of performing a training to obtain the trained machine-learning algorithm will be explained below later in connection with <FIG>.

At block <NUM>, at least one toxicity indicator is generated based on the received data via the trained machine-learning algorithm. The at least one toxicity indicator is indicative of the risks of the radiation-induced toxicity and thereby shows a prediction of potential level of radiation-induced toxicity. Details have been explained in connection with TAB.

Optionally, the method <NUM> may further comprise, at block <NUM>, applying imputation and/or normalization to the received data before applying the trained machine-learning algorithm to the received data.

Imputation is the process of replacing missing data with substituted values. When the received data miss a certain amount of information, such as a pixel value of a pre-radiotherapy-treatment image, imputation may be used to determine the missed pixel value based on pixel values of pixels surrounding the pixel. Imputation may comprise hotdeck imputation, Cold-deck imputation, mean imputation, Regression imputation, and multiple imputation.

Normalization may comprise batch normalization, weight normalization, layer normalization, instance normalization, group normalization, batch renormalization, batch-instance normalization, and so on.

Optionally, the method <NUM> may further comprise, at block <NUM>, assessing whether the at least one generated toxicity indicator indicates acceptable radiation-induced toxicity, i.e., the predefined dose map is acceptable (or optimal), based on the at least one generated toxicity indicator.

Such an assessment may be performed by comparing each of the at least one generated toxicity indicator with a corresponding threshold of respective toxicity indicator. Other predefined rules may be used. Patient-specific rules could be applied.

Optionally, when at least one generated toxicity indicator, i.e. the predefined dose map, is not acceptable (or suboptimal), the method <NUM> may additionally comprise, at block <NUM>, adjusting the predefined dose map based on the at least one generated toxicity indicator to generate a further predefined dose map. Then, the method <NUM> can be iteratively executed by replacing the predefined dose map with the further predefined dose map until acceptable (or optimal) toxicity indicators, i.e. predefined dose map, are generated.

For example, in the beginning, a predefined dose map M1 is received and processed by the trained machine-learning algorithm together with the other data, such as the pre-radiotherapy-treatment imaging data, and thereby a toxicity indicator T1 is generated by the trained machine-learning algorithm. The toxicity indicator T1 may be assessed by experienced radiation oncologists or by comparing with a threshold of toxicity indicator value, to determine whether the predefined dose map M1 is optimal or not. An automated analysis may be used instead.

If the predefined dose map M1 is determined to be suboptimal, a further predefined dose map M2 is generated by adjusting the previous predefined dose map M1 based on the previous toxicity indicator T1. Then, this adjusted dose map M2 is used to predict the risks of the radiation-induced toxicity with the respectively defined radiotherapy treatment, and thereby a further toxicity indicator T2 is generated by the trained machine-learning algorithm. The toxicity indicator T2 may be assessed in the same manner as T1.

The above-mentioned actions - generating a further predefined dose map Mn, receiving the further dose map Mn, processing the further dose map Mn together with the same other data by the same trained machine-learning algorithm, generating a further toxicity indicator Tn, and assessing the further toxicity indicator Tn - may be iteratively executed until the optimal (or maybe suboptimal) predefined dose map is determined.

<FIG> schematically illustrates an exemplary implementation of a machine-learning algorithm that can be used in the various examples described herein. The machine-learning algorithm is implemented as a neural network <NUM>. The neural network <NUM> comprises an encoder <NUM> for extracting pertinent features <NUM> of input data <NUM> (such as data associated with the ROI <NUM>) and a classifier <NUM> for generating at least one toxicity indicator <NUM> based on the extracted pertinent features <NUM> of the input data <NUM>.

After performing a training of the neural network <NUM> - e.g., by using supervised learning, semi-supervised learning, or reinforcement learning - to determine parameters and possibly hyper-parameters of the neural network <NUM>, the trained neural network <NUM> can be utilized to process the data <NUM> (cf. <NUM>) associated with the ROI <NUM> to determine at least one toxicity indicator indicative of the risks of the radiation-induced toxicity. Details with respect to the training are described in connection with <FIG>.

<FIG> is a flowchart of a method <NUM> for performing a training of a machine-learning algorithm (such as the neural network <NUM> as shown in <FIG>). The trained machine-learning algorithm can then be used for the method <NUM> of <FIG>. The method <NUM> may be executed by the same computing device that executes the method <NUM>, or by a different computing device.

The method <NUM> is a computer-implemented method of performing a training of a machine-learning algorithm for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region (e.g., <NUM> according to <FIG>) of a patient.

In detail, at block <NUM>, multiple instances of training data associated with a ROI (e.g., ROI <NUM> according to <FIG>) comprising the target region (e.g., <NUM> according to <FIG>) are received. Also, multiple instances of reference data are received, each one of the multiple instances of the reference data corresponding to a respective instance of the training data. Each one of the multiple instances of the training data comprises a dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the ROI <NUM>. Each instance of the multiple instances of the reference data comprises at least one diagnosed toxicity indicator indicative of diagnosed risks of radiation-induced toxicity - thus serving as a ground truth.

The multiple instances define a training dataset. Typically, a larger training dataset can be helpful to achieve more accurate training.

The multiple instances of training data may comprise pre-radiotherapy-treatment data associated with various patients suffering from the same type of cancers/tumors, which ensure that the ROI <NUM> and the target region <NUM> have the same or at least similar anatomical and physiological characteristics and thereby the multiple instances of training data may be regarded as instances based on the same probability distribution. The multiple instances of reference data may comprise post-radiotherapy-treatment data associated with the same various patients suffering from the same type of cancers/tumors and obtained after undergoing the radiotherapy treatment. The multiple instances of training data and the multiple instances of reference data may have a one-to-one correspondence (or bijection).

Similar to the received data according to method <NUM>, each one of the multiple instances of the training data may further comprise at least one of a dose level (e.g., expressed in units of Gray) of the radiotherapy treatment, a clinical-stage of a tumor located in the target region, comorbidities of a further patient, and/or demographics of the further patient. Respective examples have been described in connection with TAB.

The at least one diagnosed toxicity indicator - details have been explained in connection with TAB. <NUM> - may obtained by manually or automatically analyzing the post-radiotherapy-treatment data.

In some examples it is possible that the at least one diagnosed toxicity indicator corresponding to each instance of the multiple instances of the reference data is generated by a further trained machine-learning algorithm based on diagnosed data obtained at two or more time points after the radiotherapy treatment, and each one of the diagnosed data comprises imaging data of the ROI. Here, it would be possible to use techniques disclosed in "Machine Learning Automatically Detects COVID-<NUM> using Chest CTs in a Large Multicenter Cohort" arXiv:<NUM>. In particular, it would be possible to consider changes in the diagnosed data for the different time points. Such changes can be indicative of toxicity, e.g., due to changes in the tissue. For instance, it would be possible to compare the imaging data of the ROI obtained at the two or more time points, e.g., in a pixel wise manner upon performing a registration. Then, changes in the contest can be indicative of radiation-induced toxicity.

Accordingly, the method <NUM> may further comprise performing a registration of the imaging data of the ROI obtained at the two or more time points with each other or with the pre-radiotherapy-treatment imaging data.

At block <NUM>, the multiple instances of the training data are processed using the machine-learning algorithm. Optionally, the method <NUM> may further comprise applying imputation and/or normalization to each one of the multiple instances of the training data before processing the multiple instances of the training data by the machine-learning algorithm.

At block <NUM>, at least one respective estimated toxicity indicator indicative of estimated risks of radiation-induced toxicity is generated by the machine-learning algorithm, for each one of the multiple instances of the training data. The at least one estimated toxicity indicator may be regarded as an estimate of the at least one diagnosed toxicity indicator - according to the current training state of the machine-learning algorithm.

At block <NUM>, the computing device performs the training of the machine-learning algorithm by updating parameter values of the machine-learning algorithm based on a comparison between the diagnosed toxicity indicators and corresponding estimated toxicity indicators. The larger a deviation, the poorer the respective training state of the machine-learning algorithm. A loss function can be defined. Optimization techniques can be employed to adjust the parameters, e.g., backpropagation, etc..

Referring to <FIG>, the neural network <NUM> can comprise a decoder <NUM>. Using the decoder <NUM>, the training can be facilitated. Based on an output <NUM> of the decoder <NUM>, it is possible to adjust parameters of both the encoder <NUM> and the classifier <NUM> when performing the training. Thereby, a better performance of the classifier <NUM> can be achieved, i.e., the at least one toxicity indicator <NUM> can be accurately determined.

The decoder <NUM> may generate reconstructed input data as the output <NUM>. The reconstructed input data of the output <NUM> corresponds to the reference data. Alternatively, the decoder <NUM> may generate multiple instances of estimated radiomic features as the output <NUM> of the ROI based on the pertinent features <NUM> of the training data. The estimated radiomic features of the output <NUM> may comprise intensity, geometry, texture, and wavelet features of the ROI.

As described above, the encoder <NUM> and the classifier <NUM> may be trained jointly based on a classification loss Cl, i.e. by updating parameter values of both the encoder <NUM> and the classifier <NUM> based on a comparison between the diagnosed toxicity indicators and corresponding estimated toxicity indicators.

Alternatively or additionally, the encoder <NUM>, the classifier <NUM> and the decoder <NUM> may be trained jointly based on a sum or a weighted sum of the classification loss Cl and a reconstruction loss Rl, i.e. Cl+Rl or w1*Cl+w2*Rl, wherein w1 and w2 are manually selected or hyperparameters adjusted during the training. Once the training is done, only the encoder <NUM> and the classifier <NUM> will be used to predict risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient, for example by executing the method <NUM>.

Thus, the method <NUM> may optionally further comprises generating reconstructed data based on the training data using the decoder <NUM> of the machine-learning algorithm. Then, the updating of the parameter values of the machine-learning algorithm is further based on a comparison between each one of the multiple instances of the training data and corresponding reconstructed data.

Alternatively, the method <NUM> may optionally comprise receiving multiple instances of diagnosed radiomic features of the ROI, for example together with various data received at block <NUM>, and generates multiple instances of estimated radiomic features of the ROI via the decoder <NUM>. The diagnosed radiomic features may be extracted from gross tumour volumes encompassing regions of interest. The diagnosed radiomic features may comprise intensity, geometry, texture, and wavelet features of the ROI. The intensity features quantified the first-order statistical distribution of the voxel intensities within the gross tumor volumes. The geometry features quantified shape characteristics of the tumor. The texture features described spatial distribution of the voxel intensities, thereby quantifying the intratumoral heterogeneity. The intensity and texture features may be also computed after applying wavelet transformations to the original image. The diagnosed radiomic features may be handcrafted by experts or computed based on predefined mathematical formulas. The machine-learning algorithm further comprises the decoder <NUM> for generating the estimated radiomic features of the ROI based on the extracted pertinent features of the ROI. The updating of the parameter values of the machine-learning algorithm is further based on a comparison between each one of the multiple instances of the diagnosed radiomic features of the ROI and corresponding estimated radiomic features of the ROI.

<FIG> schematically illustrates a device <NUM> according to various examples. The device <NUM> may be the computing device mentioned above. The device <NUM> comprises a processing unit <NUM>, a memory unit <NUM> and an input/output interface <NUM>. The processing unit <NUM> is configured to execute a program stored in the memory unit <NUM> to perform the method <NUM> and/or the method <NUM>. The input/output interface <NUM> may communicate with actuators associated with the dosimetry system <NUM> and/or the collimation system <NUM> to adjust radiation doses of a radiotherapy treatment.

Summarizing, above, techniques have been described that facilitate predicting - prior to a radiotherapy treatment - a patient's vulnerability to radiation-induced toxicity and potential manifestation of radiation-induced toxicity after the radiotherapy by utilizing artificial intelligence (AI) techniques to extract pertinent features (i.e. disease fingerprint) from patient imaging data acquired at planning phase. This task-specific fingerprint is computed directly from imaging data of patients with similar/the same disease and treatment. Therefore, it only includes information closely related to the radiation-induced toxicity. These fingerprints are different from classical radiomics features as they are not generic measurements and are trained to be most discriminative for specific conditions or events. The algorithm also learns the causality between applied dose and tissue changes in the ROI and thereby predicts the possible extension of radiotoxicity.

In particular, patients to be treated with radiotherapy can be stratified based on a likelihood of toxicity as indicated by at least one toxicity indicator and high-risk patients could be flagged to be managed differently. Furthermore, a dose level and/or a dose map as input could be adjusted to compute a new toxicity score to a certain threshold for example. offering a dose prescription solution that uses data from a large cohort of outcome matched patients with known planning dose and toxicity profile to help an oncologist find an optimal dose with respect both outcome and toxicity profile for a specific patient.

Claim 1:
A computer-implemented method for predicting risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region (<NUM>) of a patient, the method comprising:
- receiving (<NUM>) data (<NUM>) associated with a region of interest (<NUM>) comprising the target region (<NUM>), wherein the received data (<NUM>) comprises a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data (<NUM>) of the region of interest (<NUM>);
- applying (<NUM>) a trained machine-learning algorithm to the received data; and
- generating (<NUM>), by the trained machine-learning algorithm, at least one toxicity indicator (<NUM>) based on the received data, wherein the at least one toxicity indicator (<NUM>) is indicative of the risks of the radiation-induced toxicity,
wherein the trained machine-learning algorithm is trained using the method of any one of claims <NUM>-<NUM>.