Salvage stereotactic reirradiation for intraprostatic cancer recurrence: A large retrospective study.

INTRODUCTION: Prostate cancer is the most common cancer in men. Thirty to forty-seven percent of patients treated with exclusive radiotherapy for prostate cancer will experience intraprostate recurrence. The use of radiotherapy in stereotactic conditions allows millimetric accuracy in irradiation to the target zone that minimizes the dose to organs at risk. In this study, we evaluated the clinical outcome of prostatic reirradiation with stereotactic body radiation therapy (SBRT) in patients with intraprostatic recurrence initially treated by radiotherapy. METHOD: This single-center retrospective study included 41 patients diagnosed with exclusive local recurrence of prostate cancer after radiotherapy and treatedby stereotactic Cyberknife irradiation. The objective of this study was to assess the efficacy and the safety of stereotactic reirradiation for patients with intraprostatic recurrence initially treated with radiotherapy. RESULTS: Median follow-up was 35 months. The 2-year biochemical relapse-free survival was 72.89%, the 2-year local recurrence free survival was 93.59%, the 2-year local regional recurrence-free survival was 85.24%, and the 2-year metastasis-free survival was to 91.49%. The analysis of toxicities showed a good tolerance of stereotactic irradiation. Urinary and gastro-intestinal adverse events was mostly of grades 1-2 (CTCAEv4). Grade 3 toxicity occurred in one to two patients. CONCLUSION: Stereotactic reirradiation appears effective and well-tolerated for local recurrence of prostate cancer and might allow to delay the introduction of hormonal therapy and its side effects.

Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine.

OBJECTIVE: The success of treatment planning relies critically on our ability to predict the potential benefit of a therapy. In colorectal cancer (CRC), several nomograms are available to predict different outcomes based on the use of tumour specific features. Our objective is to provide an accurate and explainable prediction of the risk to die within 10 years after CRC diagnosis, by incorporating the tumour features and the patient medical and demographic information. DESIGN: In the prostate, lung, colorectal and ovarian cancer screening (PLCO) Trial, participants (n=154 900) were randomised to screening with flexible sigmoidoscopy, with a repeat screening at 3 or 5 years, or to usual care. We selected patients who were diagnosed with CRC during the follow-up to train a gradient-boosted model to predict the risk to die within 10 years after CRC diagnosis. Using Shapley values, we determined the 20 most relevant features and provided explanation to prediction. RESULTS: During the follow-up, 2359 patients were diagnosed with CRC. Median follow-up was 16.8 years (14.4-18.9) for mortality. In total, 686 patients (29%) died from CRC during the follow-up. The dataset was randomly split into a training (n=1887) and a testing (n=472) dataset. The area under the receiver operating characteristic was 0.84 (±0.04) and accuracy was 0.83 (±0.04) with a 0.5 classification threshold. The model is available online for research use. CONCLUSIONS: We trained and validated a model with prospective data from a large multicentre cohort of patients. The model has high predictive performances at the individual scale. It could be used to discuss treatment strategies.

Long-term outcomes after bladder-preserving tri-modality therapy for patients with muscle-invasive bladder cancer.

PURPOSE: To evaluate trimodal conservative treatment as an alternative to radical surgery for urothelial muscle-invasive bladder cancer (MIBC). PATIENTS AND METHODS: This retrospective study reported the carcinologic and functional results of patients (pts) presenting a cT2/T3 N0M0 operable MIBC and fit for surgery, treated by a conservative strategy. Treatment consisted of a transurethral resection (TURB) followed by concomitant bi-fractionated split-course radiochemotherapy (RCT) with 5FU-Cisplatine. A control cystoscopy was performed six weeks after the induction RCT (eq45Gy) with systematic biopsies. Patients with complete histologic response achieved RCT protocol. Salvage surgery was proposed to pts with persistent tumor. RESULTS: 313 pts (83% cT2 and 17% cT3) treated between 1988 and 2013 were included, with a median follow-up of 59 months and 67-year mean age. After the induction RCT, the histologic response rate was 83%. After five years, overall, disease-free, and functional bladder-intact survival rates were respectively 69%, 61%, and 69%, significantly better for pts in complete response after induction RCT. Late urinary and digestive toxicities were limited, with respective rates of 4% and 1.5% of grade 3 toxicity. CONCLUSION: Trimodal strategy with RCT after TURB showed interesting functional and oncologic results and should be considered as an alternative to surgery in well-selected pts.

A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial.

BACKGROUND: The data regarding the use of conversational agents in oncology are scarce. OBJECTIVE: The aim of this study was to verify whether an artificial conversational agent was able to provide answers to patients with breast cancer with a level of satisfaction similar to the answers given by a group of physicians. METHODS: This study is a blind, noninferiority randomized controlled trial that compared the information given by the chatbot, Vik, with that given by a multidisciplinary group of physicians to patients with breast cancer. Patients were women with breast cancer in treatment or in remission. The European Organisation for Research and Treatment of Cancer Quality of Life Group information questionnaire (EORTC QLQ-INFO25) was adapted and used to compare the quality of the information provided to patients by the physician or the chatbot. The primary outcome was to show that the answers given by the Vik chatbot to common questions asked by patients with breast cancer about their therapy management are at least as satisfying as answers given by a multidisciplinary medical committee by comparing the success rate in each group (defined by a score above 3). The secondary objective was to compare the average scores obtained by the chatbot and physicians for each INFO25 item. RESULTS: A total of 142 patients were included and randomized into two groups of 71. They were all female with a mean age of 42 years (SD 19). The success rates (as defined by a score >3) was 69% (49/71) in the chatbot group versus 64% (46/71) in the physicians group. The binomial test showed the noninferiority (P<.001) of the chatbot's answers. CONCLUSIONS: This is the first study that assessed an artificial conversational agent used to inform patients with cancer. The EORTC INFO25 scores from the chatbot were found to be noninferior to the scores of the physicians. Artificial conversational agents may save patients with minor health concerns from a visit to the doctor. This could allow clinicians to spend more time to treat patients who need a consultation the most. TRIAL REGISTRATION: Clinicaltrials.gov NCT03556813, https://tinyurl.com/rgtlehq.

Evaluation of the Theoretical Teaching of Postgraduate Radiation Oncology Medical Residents in France: a Cross-Sectional Study.

This study's purpose was to have residents evaluate Radiation Oncology (RO) theoretical teaching practices in France. An anonymous electronically cross-functional survey on theoretical teaching practices in the RO residents was conducted by (i) collecting data from residents in the medical faculties in France, (ii) comparing the data across practices when possible and (iii) suggesting means of improvement. A total of 103 out of 140 RO residents responded to the survey (73.5% response rate). National, inter-university, university and internships courses do not exist in 0% (0), 16.5% (17), 53.4% (55) and 40.8% (42) of residents, respectively. Residents need additional training due to the shortage of specialised postgraduate degree training (49.5% (51)), CV enhancement to obtain a post-internship position (49.5% (51)) or as part of a career plan (47.6% (49)). The topics covered in teaching to be improved were the following: basic concept 61.2% (63), advanced concept 61.2 (63) and discussion of frequent clinical cases 50.5% (52). The topics not covered in teaching to be improved were the following: the development of career (66.0% (68)), medical English (56.3% (58)), the organisation of RO speciality (49.5% (51)) and the hospital management of RO department (38.8% (40)). This is the first national assessment of theoretical teaching of RO residents in France.

Personalized radiation therapy and biomarker-driven treatment strategies: a systematic review.

Radiosensitivity varies to a great extent across tumor types and also between patients bearing the same type of tumor. Radiation oncology pioneered the field of biomarkers with attempts to correlate tumor response to clonogenic survival, tumor potential doubling time (Tpot), and PaO2. Biomarkers predicting the clinical outcome after radiotherapy are already available, but their levels of evidence are heterogeneous. In light of these molecular factors, the issue of personalized radiation therapy in combination or as a standalone modality is addressed. Known molecular prognostic and predictive biomarkers and their present or potential respective therapeutic implications are described for six tumor types where radiotherapy is considered to be part of the mainstay: chemoradiation (e.g., gliomas, head and neck, cervical cancer), radiotherapy with or without androgen deprivation (e.g., prostate), neo-adjuvant chemoradiation (e.g., rectum), or adjuvant radiotherapy (e.g., breast).

Pilot applications of GPT-4 in radiation oncology: Summarizing patient symptom intake and targeted chatbot applications.

This study explores using GPT-4 for radiation toxicity monitoring in prostate cancer treatments. Two methods were tested: a summarization method and a chatbot interface. Surveyed radiation oncologists preferred the summarization method for its accuracy and potential for adoption (median rating 8 vs 4, p =.002). Both methods saved time.

Deep learning for automated segmentation in radiotherapy: a narrative review.

The segmentation of organs and structures is a critical component of radiation therapy planning, with manual segmentation being a laborious and time-consuming task. Interobserver variability can also impact the outcomes of radiation therapy. Deep neural networks have recently gained attention for their ability to automate segmentation tasks, with convolutional neural networks (CNNs) being a popular approach. This article provides a descriptive review of the literature on deep learning (DL) techniques for segmentation in radiation therapy planning. This review focuses on five clinical sub-sites and finds that U-net is the most commonly used CNN architecture. The studies using DL for image segmentation were included in brain, head and neck, lung, abdominal, and pelvic cancers. The majority of DL segmentation articles in radiation therapy planning have concentrated on normal tissue structures. N-fold cross-validation was commonly employed, without external validation. This research area is expanding quickly, and standardization of metrics and independent validation are critical to benchmarking and comparing proposed methods.

Artificial intelligence in radiotherapy: Current applications and future trends.

Radiation therapy has dramatically changed with the advent of computed tomography and intensity modulation. This added complexity to the workflow but allowed for more precise and reproducible treatment. As a result, these advances required the accurate delineation of many more volumes, raising questions about how to delineate them, in a uniform manner across centers. Then, as computing power improved, reverse planning became possible and three-dimensional dose distributions could be generated. Artificial intelligence offers the opportunity to make such workflow more efficient while increasing practice homogeneity. Many artificial intelligence-based tools are being implemented in routine practice to increase efficiency, reduce workload and improve homogeneity of treatments. Data retrieved from this workflow could be combined with clinical data and omic data to develop predictive tools to support clinical decision-making process. Such predictive tools are at the stage of proof-of-concept and need to be explainatory, prospectively validated, and based on large and multicenter cohorts. Nevertheless, they could bridge the gap to personalized radiation oncology, by personalizing oncologic strategies, dose prescriptions to tumor volumes and dose constraints to organs at risk.

Radiation Therapy for Locally Advanced Prostate Cancer: How Do We Treat Patients with cN1 Disease?

Radiotherapy (RT) for high-risk localized prostate cancer (HRLPC) can be controversial in the context of increasing detection of suspicious lymph nodes via advanced imaging techniques. The EORTC 22683 trial initially established RT with androgen deprivation therapy (ADT) as the standard of care for HRLPC, but many patients remain uncured. GETUG-AFU-12 showed that addition of docetaxel and estramustine to ADT improved relapse-free survival but not overall survival. STAMPEDE later demonstrated that abiraterone acetate with ADT and RT significantly improved failure-free survival and overall survival. Ongoing trials such as ENZARAD, ATLAS, DASL-HiCap, and GETUG-P17 ALADDIN are investigating the efficacy of new androgen receptor pathway inhibitors combined with RT and ADT. These studies aim to refine treatment strategies for HRLPC, particularly in the context of advanced imaging and patient upstaging. PATIENT SUMMARY: Addition of newer medications to standard radiation therapy has shown promise in improving survival for men with high-risk prostate cancer. Ongoing studies are testing these options to find the best combination. The aim is to increase the chances of curing prostate cancer, especially as advanced scan techniques are detecting more cases.

Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example.

BACKGROUND AND PURPOSE: Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers. MATERIALS AND METHODS: The TRIPOD and PROBAST checklist items were discussed and modified using a Delphi process. After consensus was reached, 2 groups of 3 co-authors scored 2 articles to evaluate usability and further optimize the adapted checklists. Finally, 10 articles were scored by all co-authors. Fleiss' kappa was calculated to assess the reliability of agreement between observers. RESULTS: Three of the 37 TRIPOD items and 5 of the 32 PROBAST items were deemed irrelevant. General terminology in the items (e.g., multivariable prediction model, predictors) was modified to align with AI-specific terms. After the first scoring round, further improvements of the items were formulated, e.g., by preventing the use of sub-questions or subjective words and adding clarifications on how to score an item. Using the final consensus list to score the 10 articles, only 2 out of the 61 items resulted in a statistically significant kappa of 0.4 or more demonstrating substantial agreement. For 41 items no statistically significant kappa was obtained indicating that the level of agreement among multiple observers is due to chance alone. CONCLUSION: Our study showed low reliability scores with the adapted TRIPOD and PROBAST checklists. Although such checklists have shown great value during development and reporting, this raises concerns about the applicability of such checklists to objectively score scientific articles for AI applications. When developing or revising guidelines, it is essential to consider their applicability to score articles without introducing bias.

Fully automated radiotherapy treatment planning: A scan to plan challenge.

BACKGROUND AND PURPOSE: Over the past decade, tools for automation of various sub-tasks in radiotherapy planning have been introduced, such as auto-contouring and auto-planning. The purpose of this study was to benchmark what degree of automation is possible. MATERIALS AND METHODS: A challenge to perform automated treatment planning for prostate and prostate bed radiotherapy was set up. Participants were provided with simulation CTs and a treatment prescription and were asked to use automated tools to produce a deliverable radiotherapy treatment plan with as little human intervention as possible. Plans were scored for their adherence to the protocol when assessed using consensus expert contours. RESULTS: Thirteen entries were received. The top submission adhered to 81.8% of the minimum objectives across all cases using the consensus contour, meeting all objectives in one of the ten cases. The same system met 89.5% of objectives when assessed with their own auto-contours, meeting all objectives in four of the ten cases. The majority of systems used in the challenge had regulatory clearance (Auto-contouring: 82.5%, Auto-planning: 77%). Despite the 'hard' rule that participants should not check or edit contours or plans, 69% reported looking at their results before submission. CONCLUSIONS: Automation of the full planning workflow from simulation CT to deliverable treatment plan is possible for prostate and prostate bed radiotherapy. While many generated plans were found to require none or minor adjustment to be regarded as clinically acceptable, the result indicated there is still a lack of trust in such systems preventing full automation.

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy.

BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.

BACKGROUND: We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC. METHOD: We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort. RESULTS: A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset. CONCLUSIONS AND RELEVANCE: We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.

[Nephron sparing in the management of localized solid renal mass]. / Épargne néphronique dans la prise en charge des tumeurs rénales malignes solides localisées.

Managing a malignant renal tumor requires, first of all, a reflection on the necessity of its treatment. It must consider the renal function, altered at the time of diagnosis in 50% of cases. The treatment method chosen depends on many factors, in particular, the predicted residual renal function, the risk of chronic kidney disease, the need for temporary or long-term dialysis, and overall long-term survival. Other factors include the size, position, and number of tumors and a hereditary tumor background. When a renal-sparing management alternative is available, total nephrectomy should no longer be performed in patients with small malignant renal masses (cT1a). This may consist of surgery (partial nephrectomy or lumpectomy), percutaneous thermo-ablation (by radiofrequency, microwave, or cryotherapy). In patients with limited life expectancy, imaging-based surveillance may be proposed to suggest treatment in case of local progression. Good coordination between urologist, radiologist, nephrologist, and sometimes radiotherapist should allow optimal management of patients with a malignant renal tumor with or without underlying renal failure.

The French Society of Young Radiation Oncologists: History, goals and perspective.

The SFjRO was created ten years ago to promote radiation oncology teaching in France. Our society has now more than 120 members from all around the country. Each year, two national courses are organized where all members are invited.

Ethical Aspects of Artificial Intelligence in Radiation Oncology.

Radiation oncology is a field that heavily relies on new technology. Data science and artificial intelligence will have an important role in the entire radiotherapy workflow. A new paradigm of routine healthcare data reuse to automate treatments and provide decision support is emerging. This review will discuss the ethical aspects of the use of artificial intelligence (AI) in radiation oncology. More specifically, the review will discuss the evolution of work through the ages, as well as the impact AI will have on it. We will then explain why AI opens a new technical era for the field and we will conclude on the challenges in the years to come.

Assessing the performances of a chatbot to collect real-life data of patients suffering from primary headache disorders.

Background: There are many scales for screening the impact of a disease. These scales are generally used to diagnose or assess the type and severity of a disease and are carried out by doctors. The chatbot helps patients suffering from primary headache disorders through personalized text messages. It could be used to collect patient-reported outcomes. Objective: The aims of this study were (1) to study whether the collection and analysis of remote scores, without prior medical intervention, are possible by a chatbot, (2) to perform suggested diagnosis and define the type of headaches, and (3) to assess the patient satisfaction and engagement with the chatbot. Method: Voluntary users of the chatbot were recruited online. They had to be over 18 and have a personal history of headaches. A questionnaire was presented (1) by text messages to the participants to evaluate migraines (2) based on the criteria of the International Headache Society. Then, the Likert scale (3) was used to assess overall satisfaction with the use of the chatbot. Results: We included 610 participants with primary headache disorders. A total of 89.94% (572/610) participants had fully completed the questionnaire (eight items), 4.72% (30/610) had partially completed it, and 5.41% (33) had refused to complete it. Statistical analysis was performed on 86.01% (547/610) of participants. Auto diagnostic showed that 14.26% (78/547) participants had a tension headache, and 85.74% (469/547) had a probable migraine. In this population, 15.78% (74/469) suffered from migraine without probable aura, and 84.22% (395/469) had migraine without aura. The patient's age had a significant incidence regarding the auto diagnosis (P = .008<.05). The evaluation of overall satisfaction shows that a total of 93.9% (599/610) of users were satisfied or very satisfied regarding the timeliness of responses the chatbot provides. Conclusion: The study confirmed that it was possible to obtain such a collection remotely, and quickly (average time of 3.24 min) with a high success rate (89.67% (547/610) participants who had fully completed the IHS questionnaire). Users were strongly engaged through chatbot: out of the total number of participants, we observed a very low number of uncompleted questionnaires (6.23% (38/610)). Conversational agents can be used to remotely collect data on the nature of the symptoms of patients suffering from primary headache disorders. These results are promising regarding patient engagement and trust in the chatbot.

Stereotactic Body Radiation Therapy for the Management of Hepatocellular Carcinoma: Efficacy and Safety.

This study aimed to describe patient characteristics, treatment efficacy, and safety in patients with hepatocellular carcinoma (HCC) undergoing stereotactic body radiation therapy (SBRT). We retrospectively analyzed data of 318 patients with 375 HCC treated between June 2007 and December 2018. Efficacy (overall survival [OS], relapse-free survival, and local control) and acute and late toxicities were described. The median follow-up period was 70.2 months. Most patients were treated with 45 Gy in three fractions. The median (range) PTV volume was 90.7 (2.6-1067.6) cc. The local control rate at 24 and 60 months was 94% (91-97%) and 94% (91-97%), respectively. Relapse-free survival at 12, 24, and 60 months was 62% (55-67%), 29% (23-36%), and 13% (8-19%), respectively. OS at 12, 24, and 60 months was 72% (95%CI 67-77%), 44% (38-50%), and 11% (7-15%), respectively. Approximately 51% and 38% experienced acute and late toxicity, respectively. Child-Pugh score B-C, high BCLC score, portal thrombosis, high GTV volume, and higher PTV volume reported on total hepatic volume ratio were significantly associated with OS. SBRT is efficient for the management of HCC with a favorable toxicity profile. The outcome is highly related to the natural evolution of the underlying cirrhosis.