Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer.

Background and Purpose: Automatic segmentation methods have greatly changed the RadioTherapy (RT) workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features. Methods and materials: We trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coefficient ( DSC ) < T and DSC ⩾ T . Two distinct cohorts of T2-Weighted (T2W) pre-RT MR images captured in 2D sequences were used: one retrospective cohort consisting of 115 LACC patients from 30 scanners, and the other prospective cohort, comprising 51 patients from 7 scanners, used for testing. Results: Segmentation by 2D-SegResNet achieved the best DSC, Surface DSC ( SDSC 3 mm ), and 95th Hausdorff Distance (95HD): DSC = 0.72 ± 0.16, SDSC 3 mm =0.66 ± 0.17, and 95HD = 14.6 ± 9.0 mm without missing segmentation ( M =0) on the test cohort. Failure detection could generate precision ( P = 0.88 ), recall ( R = 0.75 ), F1-score ( F = 0.81 ), and accuracy ( A = 0.86 ) using Logistic Regression (LR) classifier on the test cohort with a threshold T = 0.67 on DSC values. Conclusions: Our study revealed that segmentation accuracy varies slightly among different DL methods, with 2D networks outperforming 3D networks in 2D MRI sequences. Doctors found the time-saving aspect advantageous. The proposed failure detection could guide doctors in sensitive cases.

An international phase II trial and immune profiling of SBRT and atezolizumab in advanced pretreated colorectal cancer.

BACKGROUND: Immuno-radiotherapy may improve outcomes for patients with advanced solid tumors, although optimized combination modalities remain unclear. Here, we report the colorectal (CRC) cohort analysis from the SABR-PDL1 trial that evaluated the PD-L1 inhibitor atezolizumab in combination with stereotactic body radiation therapy (SBRT) in advanced cancer patients. METHODS: Eligible patients received atezolizumab 1200 mg every 3 weeks until progression or unmanageable toxicity, together with ablative SBRT delivered concurrently with the 2nd cycle (recommended dose of 45 Gy in 3 fractions, adapted upon normal tissue tolerance constraint). SBRT was delivered to at least one tumor site, with at least one additional measurable lesion being kept from the radiation field. The primary efficacy endpoint was one-year progression-free survival (PFS) rate from the start of atezolizumab. Sequential tumor biopsies were collected for deep multi-feature immune profiling. RESULTS: Sixty pretreated (median of 2 prior lines) advanced CRC patients (38 men [63%]; median age, 59 years [range, 20-81 years]; 77% with liver metastases) were enrolled in five centers (France: n = 4, Spain: n = 1) from 11/2016 to 04/2019. All but one (98%) received atezolizumab and 54/60 (90%) received SBRT. The most frequently irradiated site was lung (n = 30/54; 56.3%). Treatment-related G3 (no G4-5) toxicity was observed in 3 (5%) patients. Median OS and PFS were respectively 8.4 [95%CI:5.9-11.6] and 1.4 months [95%CI:1.2-2.6], including five (9%) patients with PFS > 1 year (median time to progression: 19.2 months, including 2/5 MMR-proficient). Best overall responses consisted of stable disease (n = 38; 64%), partial (n = 3; 5%) and complete response (n = 1; 2%). Immune-centric multiplex IHC and RNAseq showed that SBRT redirected immune cells towards tumor lesions, even in the case of radio-induced lymphopenia. Baseline tumor PD-L1 and IRF1 nuclear expression (both in CD3 + T cells and in CD68 + cells) were higher in responding patients. Upregulation of genes that encode for proteins known to increase T and B cell trafficking to tumors (CCL19, CXCL9), migration (MACF1) and tumor cell killing (GZMB) correlated with responses. CONCLUSIONS: This study provides new data on the feasibility, efficacy, and immune context of tumors that may help identifying advanced CRC patients most likely to respond to immuno-radiotherapy. TRIAL REGISTRATION: EudraCT N°: 2015-005464-42; Clinicaltrial.gov number: NCT02992912.

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy.

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Artificial Intelligence and Radiomics: Clinical Applications for Patients with Advanced Melanoma Treated with Immunotherapy.

Immunotherapy has greatly improved the outcomes of patients with metastatic melanoma. However, it has also led to new patterns of response and progression, creating an unmet need for better biomarkers to identify patients likely to achieve a lasting clinical benefit or experience immune-related adverse events. In this study, we performed a focused literature survey covering the application of artificial intelligence (AI; in the form of radiomics, machine learning, and deep learning) to patients diagnosed with melanoma and treated with immunotherapy, reviewing 12 studies relevant to the topic published up to early 2022. The most commonly investigated imaging modality was CT imaging in isolation (n = 9, 75.0%), while patient cohorts were most frequently recruited retrospectively and from single institutions (n = 7, 58.3%). Most studies concerned the development of AI tools to assist in prognostication (n = 5, 41.7%) or the prediction of treatment response (n = 6, 50.0%). Validation methods were disparate, with two studies (16.7%) performing no validation and equal numbers using cross-validation (n = 3, 25%), a validation set (n = 3, 25%), or a test set (n = 3, 25%). Only one study used both validation and test sets (n = 1, 8.3%). Overall, promising results have been observed for the application of AI to immunotherapy-treated melanoma. Further improvement and eventual integration into clinical practice may be achieved through the implementation of rigorous validation using heterogeneous, prospective patient cohorts.

A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers.

Locally advanced cervical cancer (LACC) and anal and oropharyngeal squamous cell carcinoma (ASCC and OPSCC) are mostly caused by oncogenic human papillomaviruses (HPV). In this paper, we developed machine learning (ML) models based on clinical, biological, and radiomic features extracted from pre-treatment fluorine-18-fluorodeoxyglucose positron emission tomography ([18F]-FDG PET) images to predict the survival of patients with HPV-induced cancers. For this purpose, cohorts from five institutions were used: two cohorts of patients treated for LACC including 104 patients from Gustave Roussy Campus Cancer (Center 1) and 90 patients from Leeds Teaching Hospitals NHS Trust (Center 2), two datasets of patients treated for ASCC composed of 66 patients from Institut du Cancer de Montpellier (Center 3) and 67 patients from Oslo University Hospital (Center 4), and one dataset of 45 OPSCC patients from the University Hospital of Zurich (Center 5). Radiomic features were extracted from baseline [18F]-FDG PET images. The ComBat technique was applied to mitigate intra-scanner variability. A modified consensus nested cross-validation for feature selection and hyperparameter tuning was applied on four ML models to predict progression-free survival (PFS) and overall survival (OS) using harmonized imaging features and/or clinical and biological variables as inputs. Each model was trained and optimized on Center 1 and Center 3 cohorts and tested on Center 2, Center 4, and Center 5 cohorts. The radiomic-based CoxNet model achieved C-index values of 0.75 and 0.78 for PFS and 0.76, 0.74, and 0.75 for OS on the test sets. Radiomic feature-based models had superior performance compared to the bioclinical ones, and combining radiomic and bioclinical variables did not improve the performances. Metabolic tumor volume (MTV)-based models obtained lower C-index values for a majority of the tested configurations but quite equivalent performance in terms of time-dependent AUCs (td-AUC). The results demonstrate the possibility of identifying common PET-based image signatures for predicting the response of patients with induced HPV pathology, validated on multi-center multiconstructor data.

Emerging and Evolving Concepts in Cancer Immunotherapy Imaging.

Criteria based on measurements of lesion diameter at CT have guided treatment with historical therapies due to the strong association between tumor size and survival. Clinical experience with immune checkpoint modulators shows that editing immune system function can be effective in various solid tumors. Equally, novel immune-related phenomena accompany this novel therapeutic paradigm. These effects of immunotherapy challenge the association of tumor size with response or progression and include risks and adverse events that present new demands for imaging to guide treatment decisions. Emerging and evolving approaches to immunotherapy highlight further key issues for imaging evaluation, such as dissociated response following local administration of immune checkpoint modulators, pseudoprogression due to immune infiltration in the tumor environment, and premature death due to hyperprogression. Research that may offer tools for radiologists to meet these challenges is reviewed. Different modalities are discussed, including immuno-PET, as well as new applications of CT, MRI, and fluorodeoxyglucose PET, such as radiomics and imaging of hematopoietic tissues or anthropometric characteristics. Multilevel integration of imaging and other biomarkers may improve clinical guidance for immunotherapies and provide theranostic opportunities.

Image-guided adaptive brachytherapy for advanced cervical cancer spreading to the bladder and/or rectum: Clinical outcome and prognostic factors.

INTRODUCTION: Refinements of brachytherapy techniques have led to better local control of locally advanced cervical cancer (LACC), especially with the development of image-guided adaptive brachytherapy (IGABT). Data on the efficacy of brachytherapy in cervical cancer spreading to adjacent organs are scarce. We report the experience of our institution in the treatment of these advanced tumors with IGABT. MATERIALS AND METHODS: Medical records of patients treated for a LACC spreading to the bladder and/or rectum between 2006 and 2020 at Gustave Roussy Institute were analyzed. Dosimetric parameters were collected and converted into 2 Gy per fraction equivalent doses, including the minimal dose received by 90% of the high-risk target volume (D90 CTVHR) and intermediate-risk target volume (D90 CTVIR), as well as the dose received by the most exposed 2 cm3 of the organs at risk. A Cox regression model was used to study the potential associations between clinical and dosimetric factors with survival endpoints and fistula formation. RESULTS AND STATISTICAL ANALYSIS: A total of 81 patients were identified. All patients received pelvic+/- para-aortic radiotherapy, 45 Gy in 25 fractions +/- boost to gross lymph nodes. Concomitant platinum-based chemotherapy was administered in 93.8% of cases. The median D90 CTVHR dose was 75.5 GyEQD2 (SD: 10.39 GyEQD2) and median CTVHR volume was 47.6 cm3 (SD: 27.9 cm3). Median bladder and rectal D2cm3 dose were 75.04 GyEQD2 (SD: 8.72 GyEQD2) and 64.07 GyEQD2 (SD: 6.68 GyEQD2). After a median follow-up of 27.62 ± 25.10 months, recurrence was found in 34/81 patients (42%). Metastatic failure was the most common pattern of relapse (n = 25). Use of a combined interstitial/intracavitary technique and D90 CTVHR ≥ 75.1 GyEQD2 were prognostic factors for OS in univariate analysis (HR = 0.24, 95%IC: 0.057-1, p = 0.023; HR = 0.2, 95%IC: 0.059-0.68, p = 0.0025, respectively). In multivariate analysis, a D90 CTVHR ≥ 75.1 GyEQD2 was significant for OS (HR = 0.23; 95%IC: 0.07, 0.78, p = 0.018). The occurrence of vesicovaginal fistula (VVF) was the most frequent pattern of local recurrence (HR = 4.6, 95%CI: 1.5-14, p = 0.01). CONCLUSION: Advances in brachytherapy modalities improved local control and survival while reducing toxicities. Enhancing local control through dose escalation and combined intracavitary/interstitial brachytherapy techniques is a major factor in patients cure probability, together with systemic intensification to better control distant events.

Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy.

PURPOSE: While there is still a significant need to identify potential biomarkers that can predict which patients are most likely to respond to immunotherapy treatments, radiomic approaches have shown promising results. The objectives of this study were to evaluate whether a previously validated radiomics signature of CD8 T-cells could predict progressions at a lesion level and whether the spatial heterogeneity of this radiomics score could be used at a patient level to assess the clinical response and survival of melanoma patients. METHODS: Clinical data from patients with advanced melanoma treated in our center with immunotherapy were retrieved. Radiomic features were extracted and the CD8 radiomics signature was applied. A progressive lesion was defined by an increase in lesion size of 20% or more. Dispersion metrics of the radiomics signature were estimated to evaluate the impact of interlesion heterogeneity on patient's response. Fine-tuned cut-offs for predicting overall survival were evaluated after splitting data into training and test sets. RESULTS: A total of 136 patients were included in this study, with 1120 segmented lesions at baseline, and 1052 lesions at first evaluation. A low CD8 radiomics score at baseline was associated with a significantly higher risk of lesion progression (AUC=0.55, p=0.0091), especially for lesions larger than >1 mL (AUC=0.59 overall, p=0.0035, with AUC=0.75, p=0.002 for subcutaneous lesions, AUC=0.68, p=0.01, for liver lesions and AUC=0.62, p=0.03 for nodes). The least infiltrated lesion according to the radiomics score of CD8 T-cells was positively associated with overall survival (training set HR=0.31, p=0.00062, test set HR=0.28, p=0.016), which remained significant in a multivariate analysis including clinical and biological variables. CONCLUSIONS: These results confirm the predictive value at a lesion level of the biologically inspired CD8 radiomics score in melanoma patients treated with anti-PD1-based immunotherapy and may be interesting to assess the disease spatial heterogeneity to evaluate the patient prognosis with potential clinical implication such as tumor selection for focal ablative therapies.

AutoComBat: a generic method for harmonizing MRI-based radiomic features.

The use of multicentric data is becoming essential for developing generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI) data used in brain oncology are often heterogeneous in terms of scanners and acquisitions, which significantly impact quantitative radiomic features. Various methods have been proposed to decrease dependency, including methods acting directly on MR images, i.e., based on the application of several preprocessing steps before feature extraction or the ComBat method, which harmonizes radiomic features themselves. The ComBat method used for radiomics may be misleading and presents some limitations, such as the need to know the labels associated with the "batch effect". In addition, a statistically representative sample is required and the applicability of a signature whose batch label is not present in the train set is not possible. This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images. Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets.

Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient's outcomes and to assess response to these promising combinations.

Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation.

In glioblastoma, the response to treatment assessment is essentially based on the 2D tumor size evolution but remains disputable. Volumetric approaches were evaluated for a more accurate estimation of tumor size. This study included 57 patients and compared two volume measurement methods to determine the size of different glioblastoma regions of interest: the contrast-enhancing area, the necrotic area, the gross target volume and the volume of the edema area. The two methods, the ellipsoid formula (the calculated method) and the manual delineation (the measured method) showed a high correlation to determine glioblastoma volume and a high agreement to classify patients assessment response to treatment according to RANO criteria. This study revealed that calculated and measured methods could be used in clinical practice to estimate glioblastoma volume size and to evaluate tumor size evolution.

Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center "Blue Sky Radiomics" study on stage III unresectable NSCLC.

BACKGROUND AND PURPOSE: In the retrospective-prospective multi-center "Blue Sky Radiomics" study (NCT04364776), we plan to test a pre-defined radiomic signature in a series of stage III unresectable NSCLC patients undergoing chemoradiotherapy and maintenance immunotherapy. As a necessary preliminary step, we explore the influence of different image-acquisition parameters on radiomic features' reproducibility and apply methods for harmonization. MATERIAL AND METHODS: We identified the primary lung tumor on two computed tomography (CT) series for each patient, acquired before and after chemoradiation with i.v. contrast medium and with different scanners. Tumor segmentation was performed by two oncological imaging specialists (thoracic radiologist and radio-oncologist) using the Oncentra Masterplan® software. We extracted 42 radiomic features from the specific ROIs (LIFEx). To assess the impact of different acquisition parameters on features extraction, we used the Combat tool with nonparametric adjustment and the longitudinal version (LongComBat). RESULTS: We defined 14 CT acquisition protocols for the harmonization process. Before harmonization, 76% of the features were significantly influenced by these protocols. After, all extracted features resulted in being independent of the acquisition parameters. In contrast, 5% of the LongComBat harmonized features still depended on acquisition protocols. CONCLUSIONS: We reduced the impact of different CT acquisition protocols on radiomic features extraction in a group of patients enrolled in a radiomic study on stage III NSCLC. The harmonization process appears essential for the quality of radiomic data and for their reproducibility. ClinicalTrials.gov Identifier: NCT04364776, First Posted:April 28, 2020, Actual Study Start Date: April 15, 2020, https://clinicaltrials.gov/ct2/show/NCT04364776 .

Brachytherapy for the Conservative Treatment of Female Peri-Urethral Carcinoma.

Introduction: Peri-urethral cancers (PUC) are rare tumors. Brachytherapy (BT), either monotherapy or combined with radiation therapy, is a preferred treatment option to spare the morbidity of surgery and achieve organ preservation. We report, to the best of our knowledge, the largest experience of brachytherapy among women with PUC. Patients and Methods: This is a retrospective review of the medical records of female patients with PUC who underwent low- or pulse-dose-rate BT with or without external beam radiotherapy at Gustave Roussy between 1990 and 2018. Patients were categorized according to the treatment intention into a primary and recurrent group. The Kaplan-Meier method was used for survival analysis, and the Cox proportional-hazard model was used for univariate analysis. Brachythewharapy-related adverse events were reported according to Common Terminology Criteria for Adverse Events version 4. Results: We identified 44 patients with PUC who underwent BT. Of the 44 patients, 22 had primary tumors and 22 had recurrent tumors. Histologies were mainly adenocarcinoma (n = 20) and squamous cell carcinoma (n = 14). The median prescribed dose was 60 Gy for the 24 patients treated with BT alone and 20 Gy (IQ range: 15-56.25 Gy) for the 20 patients treated with BT in combination with EBRT. With a median follow-up of 21.5 months (range 7.5-60.8), a total of six patients experienced local relapse (17.5%). The 2-year overall survival probability was 63% (95%CI: 49.2-81.4%). The most common toxicities were acute genito-urinary grade 1-2 toxicities. At the last follow-up, four patients experienced focal necrosis. Conclusions: In this cohort of women with PUC undergoing BT, we observed an 80% probability of local control with acceptable morbidity. Though survival was poor, with high metastatic relapse probability, BT was useful to focally escalate the dose and optimize local control in the context of an organ sparing strategy.

[Artificial intelligence and medical imaging]. / Intelligence artificielle et imagerie médicale.

The use of artificial intelligence methods for image recognition is one of the most developed branches of the AI field and these technologies are now commonly used in our daily lives. In the field of medical imaging, approaches based on artificial intelligence are particularly promising, with numerous applications and a strong interest in the search for new biomarkers. Here, we will present the general methods used in these approaches as well as the potential areas of application.

COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures.

Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitative metric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clustering methods and a referential gene signature used in the literature, confirm state of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times better mathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

Image-Guided Brachytherapy for Salvage Reirradiation: A Systematic Review.

BACKGROUND: Local recurrence in gynecological malignancies occurring in a previously irradiated field is a challenging clinical issue. The most frequent curative-intent treatment is salvage surgery. Reirradiation, using three-dimensional image-guided brachytherapy (3D-IGBT), might be a suitable alternative. We reviewed recent literature concerning 3D-IGBT for reirradiation in the context of local recurrences from gynecological malignancies. METHODS: We conducted a large-scale literature research, and 15 original studies, responding to our research criteria, were finally selected. RESULTS: Local control rates ranged from 44% to 71.4% at 2-5 years, and overall survival rates ranged from 39.5% to 78% at 2-5 years. Grade ≥3 toxicities ranged from 1.7% to 50%, with only one study reporting a grade 5 event. Results in terms of outcome and toxicities were highly variable depending on studies. Several studies suggested that local control could be improved with 2 Gy equivalent doses >40 Gy. CONCLUSION: IGBT appears to be a feasible alternative to salvage surgery in inoperable patients or patients refusing surgery, with an acceptable outcome for patients who have no other curative therapeutic options, however at a high cost of long-term grade ≥3 toxicities in some studies. We recommend that patients with local recurrence from gynecologic neoplasm occurring in previously irradiated fields should be referred to highly experienced expert centers. Centralization of data and large-scale multicentric international prospective trials are warranted. Efforts should be made to improve local control while limiting the risk of toxicities.

Pulsed Dose Rate Brachytherapy of Lip Carcinoma: Clinical Outcome and Quality of Life Analysis.

PURPOSE: Lip carcinoma represents one of the most common types of head and neck cancer. Brachytherapy is a highly effective therapeutic option for all stages of lip cancers. We report our experience of pulsed dose rate brachytherapy (PDR) as treatment of lip carcinoma. METHODS AND MATERIALS: this retrospective single center study included all consecutive patients treated for a lip PDR brachytherapy in our institution from 2010 to 2019. The toxicities and outcomes of the patients were reported, and a retrospective quality of life assessment was conducted by phone interviews (FACT H&N). RESULTS: From October 2010 to December 2019, 38 patients were treated in our institution for a lip carcinoma by PDR brachytherapy. The median age was 73, and the majority of patients presented T1-T2 tumors (79%). The median total dose was 70.14 Gy (range: 60-85 Gy). With a mean follow-up of 35.4 months, two patients (5.6%) presented local failure, and seven patients (19%) had lymph node progression. The Kaplan-Meier estimated probability of local failure was 7.2% (95% CI: 0.84-1) at two and four years. All patients encountered radiomucitis grade II or higher. The rate of late toxicities was low: three patients (8.3%) had grade II fibrosis, and one patient had grade II chronic pain. All patients would highly recommend the treatment. The median FACT H&N total score was 127 out of 148, and the median FACT H&N Trial Outcome Index was 84. CONCLUSIONS: This study confirms that an excellent local control rate is achieved with PDR brachytherapy as treatment of lip carcinoma, with very limited late side effects and satisfactory functional outcomes. A multimodal approach should help to improve regional control.

Systematic Screening of COVID-19 Disease Based on Chest CT and RT-PCR for Cancer Patients Undergoing Radiation Therapy in a Coronavirus French Hotspot.

PURPOSE: Patients with cancer are presumed to be more vulnerable to COVID-19. We evaluated a screening strategy combining chest computed tomography (CT) and reverse-transcription polymerase chain reaction (RT-PCR) for patients treated with radiation therapy at our cancer center located in a COVID-19 French hotspot during the first wave of the pandemic. METHODS AND MATERIALS: Chest CT images were proposed during radiation therapy CT simulation. Images were reviewed by an expert radiologist according to the COVID-19 Reporting and Data System classification. Nasal swabs with RT-PCR assay were initially proposed in cases of suspicious imaging or clinical context and were eventually integrated into the systematic screening. A dedicated radiation therapy workflow was proposed for COVID-19 patients to limit the risk of contamination. RESULTS: From March 18, 2020 to May 1, 2020, 480 patients were screened by chest CT, and 313 patients had both chest CT and RT-PCR (65%). The cumulative incidence of COVID-19 was 5.4% (95% confidence interval [CI], 3.6-7.8; 26 of 480 patients). Diagnosis of COVID-19 was made before radiation therapy for 22 patients (84.6%) and during RT for 4 patients (15.3%). Chest CT directly aided the diagnosis of 7 cases in which the initial RT-PCR was negative or not feasible, out of a total of 480 patients (1.5%) and 517 chest CT acquisitions. Four patients with COVID-19 at the time of the chest CT screening had a false negative CT. Sensitivity and specificity of chest CT screening in patients with both RT-PCR and chest CT testing were estimated at 0.82 (95% CI, 0.60-0.95) and 0.98 (95% CI, 0.96-0.99), respectively. Adaptation of the radiation therapy treatment was made for all patients, with 7 postponed treatments (median: 5 days; interquartile range, 1.5-14.8). CONCLUSIONS: The benefit of systematic use of chest CT screening during CT simulation for patients undergoing radiation therapy during the COVID-19 pandemic seemed limited.