Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography.

peer reviewedOver the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy

Optimizing the dosimetric planning in lung cancer patients treated with radiotherapy : development of new prediction tools using machine learning approaches

La radiothérapie pulmonaire est le traitement de référence chez les patients porteurs d’un cancer broncho-pulmonaire localement avancé, lorsque la chirurgie n'est pas possible. Environ 20 à 25 % des patients présentent une toxicité pulmonaire aigue (APT) de grade ≥ 2, ce qui peut nuire à leur qualité de vie ou à la réalisation du traitement. L'apprentissage automatique est un domaine en pleine évolution qui fournit aux chercheurs et aux cliniciens de nouveaux outils pour exploiter pleinement les données multimodales. Utilisée sur une carte de dose, la radiomique pourrait refléter l'hétérogénéité de la distribution spatiale de la dose. Une approche voxel par voxel a été développée pour identifier un sous-ensemble de voxels sensibles aux radiations. Une fois identifiée, cette sous-région pourrait être utilisée lors de la dosimétrie et réduire le risque de toxicité pulmonaire. Plusieurs modèles ont été développés pour la prédiction de l'APT ≥ grade 2. Un modèle radiomique a été capable de prédire le risque d’APT avec une précision (BAcc) de 92%. L'ajout de la dose moyenne reçue par la région Pmap définie par l'approche voxel par voxel a permis d'obtenir un modèle de prédiction robuste (BAcc 82 %) dans la cohorte de validation prospective, avec des résultats similaires pour le modèle radiomique. La combinaison de ces deux approches a amélioré la prédiction du modèle (BAcc 86 %). L'évitement de la région Pmap a réduit le risque d’APT chez 43,2% des patients. Ces modèles innovants pourraient permettre de développer de nouvelles approches dosimétriques telles que la radiothérapie par dose-painting ou des combinaisons thérapeutiques innovantes.Lung radiotherapy is the standard of care in patients diagnosed with a locally advanced lung cancer, when surgery isn’t feasible. Approximately 20-25% of the patients will experience a grade ≥ 2 acute pulmonary toxicity (APT), possibly impairing their quality of life or the treatment’s completion.Machine-learning is an evolving field providing researchers and clinicians with new tools to fully exploit the multi-modal data. Radiomics is thought to reflect heterogeneity in a volume. Used on a dose map, it could reflect the heterogeneity of the spatial dose distribution. A voxel-by-voxel approach was developed to identify significant voxel in regard to a specific endpoint and could identify a sub-set of radiation-sensitive voxels.Once identified, this sub-region could be used during the dosimetric planning as a volume to avoid and possibly reduce the risk of lung toxicity.Several models were developed for the prediction of the APT ≥ grade 2. A radiomics-model was able to predict the risk of APT with a balanced accuracy (BAcc) of 92%. Adding the Mean dose to the Pmap-region identified with the voxel-by-voxel approach resulted in a robust prediction model (BAcc 82%) in the prospective validation cohort.Combining these two approaches enhanced the model’s prediction (BAcc 86%). Functional avoidance of the Pmap region significantly reduced the APT risk in 43.2% of the patients.Several innovative APT-prediction models were developed and appear as implementable on a daily basis and efficient at reducing the risk of APT.Regional radiosensitivity should be considered in usual lung dose constraints, opening the possibility of an easily implementable adaptive dosimetry planning or developing new treatment’s approaches such as a dose-painting radiotherapy or treatments’combinations

Narrative Review of the Post-Operative Management of Prostate Cancer Patients: Is It Really the End of Adjuvant Radiotherapy?

Despite three randomized trials indicating a significant reduction in biochemical recurrence (BCR) in high-risk patients, adjuvant radiotherapy (aRT) was rarely performed, even in patients harboring high-risk features. aRT is associated with a higher risk of urinary incontinence and is often criticized for the lack of patient selection criteria. With a BCR rate reaching 30–70% in high-risk patients, a consensus between urologists and radiation oncologists was needed, leading to three different randomized trials challenging aRT with early salvage radiotherapy (eSRT). In these three different randomized trials with event-free survival as the primary outcome and a planned meta-analysis, eSRT appeared as non-inferior to aRT, answering, for some, this never-ending question. For many, however, the debate persists; these results raised several questions among urologists and radiation oncologists. BCR is thought to be a surrogate for clinically meaningful endpoints such as overall survival and cancer-specific survival but may be poorly efficient in comparison with metastasis-free survival. Imaging of rising prostate-specific antigen (PSA), post-operative persistent PSA and BCR was revolutionized by the broader use of MRI and nuclear imaging such as PET-PSMA; these imaging modalities were not analyzed in the previous randomized trials. A sub-group of very high-risk patients could possibly benefit from an adjuvant radiotherapy; but their usual risk factors such as high Gleason score or invaded surgical margins mean they are unable to be selected. More precise biomarkers of early BCR or even metastatic-relapse were developed in this setting and could be useful for the patients’ stratification. In this review, we insist on the need for multidisciplinary discussions to fully comprehend the individual characteristics of each patient and propose the best treatment strategy for every patient

Narrative Review of the Post-Operative Management of Prostate Cancer Patients: Is It Really the End of Adjuvant Radiotherapy?

Despite three randomized trials indicating a significant reduction in biochemical recurrence (BCR) in high-risk patients, adjuvant radiotherapy (aRT) was rarely performed, even in patients harboring high-risk features. aRT is associated with a higher risk of urinary incontinence and is often criticized for the lack of patient selection criteria. With a BCR rate reaching 30–70% in high-risk patients, a consensus between urologists and radiation oncologists was needed, leading to three different randomized trials challenging aRT with early salvage radiotherapy (eSRT). In these three different randomized trials with event-free survival as the primary outcome and a planned meta-analysis, eSRT appeared as non-inferior to aRT, answering, for some, this never-ending question. For many, however, the debate persists; these results raised several questions among urologists and radiation oncologists. BCR is thought to be a surrogate for clinically meaningful endpoints such as overall survival and cancer-specific survival but may be poorly efficient in comparison with metastasis-free survival. Imaging of rising prostate-specific antigen (PSA), post-operative persistent PSA and BCR was revolutionized by the broader use of MRI and nuclear imaging such as PET-PSMA; these imaging modalities were not analyzed in the previous randomized trials. A sub-group of very high-risk patients could possibly benefit from an adjuvant radiotherapy; but their usual risk factors such as high Gleason score or invaded surgical margins mean they are unable to be selected. More precise biomarkers of early BCR or even metastatic-relapse were developed in this setting and could be useful for the patients’ stratification. In this review, we insist on the need for multidisciplinary discussions to fully comprehend the individual characteristics of each patient and propose the best treatment strategy for every patient

Narrative Review of Synergistics Effects of Combining Immunotherapy and Stereotactic Radiation Therapy

Stereotactic radiotherapy (SRT) has become an attractive treatment modality in full bloom in recent years by presenting itself as a safe, noninvasive alternative to surgery to control primary or secondary malignancies. Although the focus has been on local tumor control as the therapeutic goal of stereotactic radiotherapy, rare but intriguing observations of abscopal (or out-of-field) effects have highlighted the exciting possibility of activating antitumor immunity using high-dose radiation. Furthermore, immunotherapy has revolutionized the treatment of several types of cancers in recent years. However, resistance to immunotherapy often develops. These observations have led researchers to combine immunotherapy with SRT in an attempt to improve outcomes. The benefits of this combination would come from the stimulation and suppression of various immune pathways. Thus, in this review, we will first discuss the immunomodulation induced by SRT with the promising results of preclinical studies on the changes in the immune balance observed after SRT. Then, we will discuss the opportunities and risks of the combination of SRT and immunotherapy with the preclinical and clinical data available in the literature. Furthermore, we will see that many perspectives are conceivable to potentiate the synergistic effects of this combination with the need for prospective studies to confirm the encouraging data

Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Immune checkpoint inhibitors (ICI) have revolutionized the management of locally advanced and advanced non-small lung cancer (NSCLC). With an improvement in the overall survival (OS) as both first- and second-line treatments, ICIs, and especially programmed-death 1 (PD-1) and programmed-death ligands 1 (PD-L1), changed the landscape of thoracic oncology. The PD-L1 level of expression is commonly accepted as the most used biomarker, with both prognostic and predictive values. However, even in a low expression level of PD-L1, response rates remain significant while a significant number of patients will experience hyperprogression or adverse events. The dentification of such subtypes is thus of paramount importance. While several studies focused mainly on the prediction of the PD-L1 expression status, others aimed directly at the development of prediction/prognostic models. The response to ICIs depends on a complex physiopathological cascade, intricating multiple mechanisms from the molecular to the macroscopic level. With the high-throughput extraction of features, omics approaches aim for the most comprehensive assessment of each patient. In this article, we will review the place of the different biomarkers (clinical, biological, genomics, transcriptomics, proteomics and radiomics), their clinical implementation and discuss the most recent trends projecting on the future steps in prediction modeling in NSCLC patients treated with ICI

Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective

Introduction: Segmentation of organs at risk (OARs) and target volumes need time and precision but are highly repetitive tasks. Radiation oncology has known tremendous technological advances in recent years, the latest being brought by artificial intelligence (AI). Despite the advantages brought by AI for segmentation, some concerns were raised by academics regarding the impact on young radiation oncologists’ training. A survey was thus conducted on young french radiation oncologists (ROs) by the SFjRO (Société Française des jeunes Radiothérapeutes Oncologues). Methodology: The SFjRO organizes regular webinars focusing on anatomical localization, discussing either segmentation or dosimetry. Completion of the survey was mandatory for registration to a dosimetry webinar dedicated to head and neck (H & N) cancers. The survey was generated in accordance with the CHERRIES guidelines. Quantitative data (e.g., time savings and correction needs) were not measured but determined among the propositions. Results: 117 young ROs from 35 different and mostly academic centers participated. Most centers were either already equipped with such solutions or planning to be equipped in the next two years. AI segmentation software was mostly useful for H & N cases. While for the definition of OARs, participants experienced a significant time gain using AI-proposed delineations, with almost 35% of the participants saving between 50–100% of the segmentation time, time gained for target volumes was significantly lower, with only 8.6% experiencing a 50–100% gain. Contours still needed to be thoroughly checked, especially target volumes for some, and edited. The majority of participants suggested that these tools should be integrated into the training so that future radiation oncologists do not neglect the importance of radioanatomy. Fully aware of this risk, up to one-third of them even suggested that AI tools should be reserved for senior physicians only. Conclusions: We believe this survey on automatic segmentation to be the first to focus on the perception of young radiation oncologists. Software developers should focus on enhancing the quality of proposed segmentations, while young radiation oncologists should become more acquainted with these tools

Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer

Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy

Introduction: The standard of care for people with locally advanced lung cancer (LALC) who cannot be operated on is (chemo)-radiation. Despite the application of dose constraints, acute pulmonary toxicity (APT) still often occurs. Prediction of APT is of paramount importance for the development of innovative therapeutic combinations. The two models were previously individually created. With success, the Rad-model incorporated six radiomics functions. After additional validation in prospective cohorts, a Pmap-model was created by identifying a specific region of the right posterior lung and incorporating several clinical and dosimetric parameters. To create and test a novel model to forecast the risk of APT in two cohorts receiving volumetric arctherapy radiotherapy (VMAT), we aimed to include all the variables in this study. Methods: In the training cohort, we retrospectively included all patients treated by VMAT for LALC at one institution between 2015 and 2018. APT was assessed according to the CTCAE v4.0 scale. Usual clinical and dosimetric features, as well as the mean dose to the pre-defined Pmap zone (DMeanPmap), were processed using a neural network approach and subsequently validated on an observational prospective cohort. The model was evaluated using the area under the curve (AUC) and balanced accuracy (Bacc). Results: 165 and 42 patients were enrolled in the training and test cohorts, with APT rates of 22.4 and 19.1%, respectively. The AUCs for the Rad and Pmap models in the validation cohort were 0.83 and 0.81, respectively, whereas the AUC for the combined model (Comb-model) was 0.90. The Bacc for the Rad, Pmap, and Comb models in the validation cohort were respectively 78.7, 82.4, and 89.7%. Conclusion: The accuracy of prediction models were increased by combining radiomics, DMeanPmap, and common clinical and dosimetric features. The use of this model may improve the evaluation of APT risk and provide access to novel therapeutic alternatives, such as dose escalation or creative therapy combinations