One-year mortality and causes of death after stereotactic radiation therapy for refractory ventricular arrhythmias: A systematic review and pooled analysis.

Patients treated with cardiac stereotactic body radiation therapy (radioablation) for refractory ventricular arrhythmias are patients with advanced structural heart disease and significant comorbidities. However, data regarding 1-year mortality after the procedure are scarce. This systematic review and pooled analysis aimed at determining 1-year mortality after cardiac radioablation for refractory ventricular arrhythmias and investigating leading causes of death in this population. MEDLINE/EMBASE databases were searched up to January 2023 for studies including patients undergoing cardiac radioablation for the treatment of refractory ventricular arrhythmias. Quality of included trials was assessed using the NIH Tool for Case Series Studies (PROSPERO CRD42022379713). A total of 1,151 references were retrieved and evaluated for relevance. Data were extracted from 16 studies, with a total of 157 patients undergoing cardiac radioablation for refractory ventricular arrhythmias. Pooled 1-year mortality was 32 % (95 %CI: 23-41), with almost half of the deaths occurring within three months after treatment. Among the 157 patients, 46 died within the year following cardiac radioablation. Worsening heart failure appeared to be the leading cause of death (52 %), although non-cardiac mortality remained substantial (41 %) in this population. Age≥70yo was associated with a significantly higher 12-month all-cause mortality (p<0.022). Neither target volume size nor radiotherapy device appeared to be associated with 1-year mortality (p = 0.465 and p = 0.199, respectively). About one-third of patients undergoing cardiac stereotactic body radiation therapy for refractory ventricular arrhythmias die within the first year after the procedure. Worsening heart failure appears to be the leading cause of death in this population.

Multimodal fusion workflow for target delineation in cardiac radioablation of ventricular tachycardia.

BACKGROUND: Cardiac radioablation (CR) is an innovative treatment to ablate cardiac arrythmia sources by radiation therapy. CR target delineation is a challenging task requiring the exploitation of highly different imaging modalities, including cardiac electro-anatomical mapping (EAM). PURPOSE: In this work, a data integration process is proposed to alleviate the tediousness of CR target delineation by generating a fused representation of the heart, including all the information of interest resulting from the analysis and registration of electro-anatomical data, PET scan and planning computed tomography (CT) scan. The proposed process was evaluated by cardiologists during delineation trials. METHODS: The data processing pipeline was composed of the following steps. The cardiac structures of interest were segmented from cardiac CT scans using a deep learning method. The EAM data was registered to the cardiac CT scan using a point cloud based registration method. The PET scan was registered using rigid image registration. The EAM and PET information, as well as the myocardium thickness, were projected on the surface of the 3D mesh of the left ventricle. The target was identified by delineating a path on this surface that was further projected to the thickness of the myocardium to create the target volume. This process was evaluated by comparison with a standard slice-by-slice delineation with mental EAM registration. Four cardiologists delineated targets for three patients using both methods. The variability of target volumes, and the ease of use of the proposed method, were evaluated. RESULTS: All cardiologists reported being more confident and efficient using the proposed method. The inter-clinician variability in delineated target volume was systematically lower with the proposed method (average dice score of 0.62 vs. 0.32 with a classical method). Delineation times were also improved. CONCLUSIONS: A data integration process was proposed and evaluated to fuse images of interest for CR target delineation. It effectively reduces the tediousness of CR target delineation, while improving inter-clinician agreement on target volumes. This study is still to be confirmed by including more clinicians and patient data to the experiments.

Cardiac stereotactic radiation therapy for refractory ventricular arrhythmias in patients with left ventricular assist devices.

Left ventricular assist device (LVAD) implantation is an established treatment for patients with advanced heart failure refractory to medical therapy. However, the incidence of ventricular arrhythmias (VAs) is high in this population, both in the acute and delayed phases after implantation. About one-third of patients implanted with an LVAD will experience sustained VAs, predisposing these patients to worse outcomes and complicating patient management. The combination of pre-existing myocardial substrate and complex electrical remodeling after LVAD implantation account for the high incidence of VAs observed in this population. LVAD patients presenting VAs refractory to antiarrhythmic therapy and catheter ablation procedures are not rare. In such patients, treatment options are extremely limited. Stereotactic body radiation therapy (SBRT) is a technique that delivers precise and high doses of radiation to highly defined targets, reducing exposure to adjacent normal tissue. Cardiac SBRT has recently emerged as a promising alternative with a growing number of case series reporting the effectiveness of the technique in reducing the VA burden in patients with arrhythmias refractory to conventional therapies. The safety profile of cardiac SBRT also appears favorable, even though the current clinical experience remains limited. The use of cardiac SBRT for the treatment of refractory VAs in patients implanted with an LVAD are even more scarce. This review summarizes the clinical experience of cardiac SBRT in LVAD patients and describes technical considerations related to the implementation of the SBRT procedure in the presence of an LVAD.

Segmentation of cardiac infarction in delayed-enhancement MRI using probability map and transformers-based neural networks.

BACKGROUND AND OBJECTIVE: Automatic segmentation of myocardial infarction is of great clinical interest for the quantitative evaluation of myocardial infarction (MI). Late Gadolinium Enhancement cardiac MRI (LGE-MRI) is commonly used in clinical practice to quantify MI, which is crucial for clinical diagnosis and treatment of cardiac diseases. However, the segmentation of infarcted tissue in LGE-MRI is highly challenging due to its high anisotropy and inhomogeneities. METHODS: The innovative aspect of our work lies in the utilization of a probability map of the healthy myocardium to guide the localization of infarction, as well as the combination of 2D U-Net and U-Net transformers to achieve the final segmentation. Instead of employing a binary segmentation map, we propose using a probability map of the normal myocardium, obtained through a dedicated 2D U-Net. To leverage spatial information, we employ a U-Net transformers network where we incorporate the probability map into the original image as an additional input. Then, To address the limitations of U-Net in segmenting accurately the contours, we introduce an adapted loss function. RESULTS: Our method has been evaluated on the 2020 MICCAI EMIDEC challenge dataset, yielding competitive results. Specifically, we achieved a Dice score of 92.94% for the myocardium and 92.36% for the infarction. These outcomes highlight the competitiveness of our approach. CONCLUSION: In the case of the infarction class, our proposed method outperforms state-of-the-art techniques across all metrics evaluated in the challenge, establishing its superior performance in infarction segmentation. This study further reinforces the importance of integrating a contour loss into the segmentation process.

Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

PURPOSE: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients. METHODS AND MATERIALS: Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 23 patients was retrospectively analyzed. Anatomical deformation during treatment was estimated using free-form deformation (FFD) method from Elastix and DL-based VoxelMorph approaches. The VoxelMorph method was investigated using anatomical scans (VMorph_Sc) or label images (VMorph_Msk), or the combination of both (VMorph_Sc_Msk). Accumulated doses were compared with the planning dose. RESULTS: The DSC ranges, averaged for prostate, rectum and bladder, were 0.60-0.71, 0.67-0.79, 0.93-0.98, and 0.89-0.96 for the FFD, VMorph_Sc, VMorph_Msk, and VMorph_Sc_Msk methods, respectively. When including both anatomical and label images, VoxelMorph estimated more complex deformations resulting in heterogeneous determinant of Jacobian and higher percentage of deformation vector field (DVF) folding (up to a mean value of 1.90% in the prostate). Large differences were observed between DL-based methods regarding estimation of the accumulated dose, showing systematic overdosage and underdosage of the bladder and rectum, respectively. The difference between planned mean dose and accumulated mean dose with VMorph_Sc_Msk reached a median value of +6.3 Gy for the bladder and -5.1 Gy for the rectum. CONCLUSION: The estimation of the deformations using DL-based approach is feasible for male pelvic anatomy but requires the inclusion of anatomical contours to improve organ correspondence. High variability in the estimation of the accumulated dose depending on the deformable strategy suggests further investigation of DL-based techniques before clinical deployment.

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.

Automatic segmentation for plan-of-the-day selection in CBCT-guided adaptive radiation therapy of cervical cancer.

Objective.Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, among which, at each treatment fraction, the PoD is selected using daily images. However, this strategy is limited by PoD selection uncertainties. This work aimed to propose and evaluate a workflow to automatically and quantitatively identify the PoD for cervix cancer ART based on daily CBCT images.Approach.The quantification was based on the segmentation of the main structures of interest in the CBCT images (clinical target volume [CTV], rectum, bladder, and bowel bag) using a deep learning model. Then, the PoD was selected from the treatment plan library according to the geometrical coverage of the CTV. For the evaluation, the resulting PoD was compared to the one obtained considering reference CBCT delineations.Main results.In experiments on a database of 23 patients with 272 CBCT images, the proposed method obtained an agreement between the reference PoD and the automatically identified PoD for 91.5% of treatment fractions (99.6% when considering a 5% margin on CTV coverage).Significance.The proposed automatic workflow automatically selected PoD for ART using deep-learning methods. The results showed the ability of the proposed process to identify the optimal PoD in a treatment plan library.

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.

Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography.

PURPOSE: Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years. However, they are either sensitive to large organ deformations, or require to train a convolutional neural network (CNN) from a database of delineated CBCT images, which is difficult to do without improvement of image quality. In this work, we propose an alternative approach: to train a CNN (using a deep learning-based segmentation tool called nnU-Net) from a database of artificial CBCT images simulated from planning CT, for which it is easier to obtain the organ contours. METHODS: Pseudo-CBCT (pCBCT) images were simulated from readily available segmented planning CT images, using the GATE Monte Carlo simulation. CT reference delineations were copied onto the pCBCT, resulting in a database of segmented images used to train the neural network. The studied segmentation contours were: bladder, rectum, and prostate contours. We trained multiple nnU-Net models using different training: (1) segmented real CBCT, (2) pCBCT, (3) segmented real CT and tested on pseudo-CT (pCT) generated from CBCT with cycleGAN, and (4) a combination of (2) and (3). The evaluation was performed on different datasets of segmented CBCT or pCT by comparing predicted segmentations with reference ones thanks to Dice similarity score and Hausdorff distance. A qualitative evaluation was also performed to compare DIR-based and nnU-Net-based segmentations. RESULTS: Training with pCBCT was found to lead to comparable results to using real CBCT images. When evaluated on CBCT obtained from the same hospital as the CT images used in the simulation of the pCBCT, the model trained with pCBCT scored mean DSCs of 0.92 ± 0.05, 0.87 ± 0.02, and 0.85 ± 0.04 and mean Hausdorff distance 4.67 ± 3.01, 3.91 ± 0.98, and 5.00 ± 1.32 for the bladder, rectum, and prostate contours respectively, while the model trained with real CBCT scored mean DSCs of 0.91 ± 0.06, 0.83 ± 0.07, and 0.81 ± 0.05 and mean Hausdorff distance 5.62 ± 3.24, 6.43 ± 5.11, and 6.19 ± 1.14 for the bladder, rectum, and prostate contours, respectively. It was also found to outperform models using pCT or a combination of both, except for the prostate contour when tested on a dataset from a different hospital. Moreover, the resulting segmentations demonstrated a clinical acceptability, where 78% of bladder segmentations, 98% of rectum segmentations, and 93% of prostate segmentations required minor or no corrections, and for 76% of the patients, all structures of the patient required minor or no corrections. CONCLUSION: We proposed to use simulated CBCT images to train a nnU-Net segmentation model, avoiding the need to gather complex and time-consuming reference delineations on CBCT images.

Stretched reconstruction based on 2D freehand ultrasound for peripheral artery imaging.

PURPOSE: Endovascular revascularization is becoming the established first-line treatment of peripheral artery disease (PAD). Ultrasound (US) imaging is used pre-operatively to make the first diagnosis and is often followed by a CT angiography (CTA). US provides a non-invasive and non-ionizing method for the visualization of arteries and lesion(s). This paper proposes to generate a 3D stretched reconstruction of the femoral artery from a sequence of 2D US B-mode frames. METHODS: The proposed method is solely image-based. A Mask-RCNN is used to segment the femoral artery on the 2D US frames. In-plane registration is achieved by aligning the artery segmentation masks. Subsequently, a convolutional neural network (CNN) predicts the out-of-plane translation. After processing all input frames and re-sampling the volume according to the vessel's centerline, the whole femoral artery can be visualized on a single slice of the resulting stretched view. RESULTS: 111 tracked US sequences of the left or right femoral arteries have been acquired on 18 healthy volunteers. fivefold cross-validation was used to validate our method and achieve an absolute mean error of 0.28 ± 0.28 mm and a median drift error of 8.98%. CONCLUSION: This study demonstrates the feasibility of freehand US stretched reconstruction following a deep learning strategy for imaging the femoral artery. Stretched views are generated and can give rich diagnosis information in the pre-operative planning of PAD procedures. This visualization could replace traditional 3D imaging in the pre-operative planning process, and during the pre-operative diagnosis phase, to identify, locate, and size stenosis/thrombosis lesions.

Extraction of Premature Newborns' Spontaneous Cries in the Real Context of Neonatal Intensive Care Units.

Cry analysis is an important tool to evaluate the development of preterm infants. However, the context of Neonatal Intensive Care Units is challenging, since a wide variety of sounds can occur (e.g., alarms and adult voices). In this paper, a method to extract cries is proposed. It is based on an initial segmentation between silence and sound events, followed by feature extraction on the resulting audio segments and a cry and non-cry classification. A database of 198 cry events coming from 21 newborns and 439 non-cry events was created. Then, a set of features-including Mel-Frequency Cepstral Coefficients-issued from principal component analysis, was computed to describe each audio segment. For the first time in cry analysis, noise was handled using harmonic plus noise analysis. Several machine learning models have been compared. The K-Nearest Neighbours approach showed the best results with a precision of 92.9%. To test the approach in a monitoring application, 412 h of recordings were automatically processed. The cries automatically selected were replayed and a precision of 92.2% was obtained. The impact of errors on the fundamental frequency characterisation was also studied. Results show that despite a difficult context, automatic cry extraction for non-invasive monitoring of vocal development of preterm infants is achievable.

Desynchronization Strain Patterns and Contractility in Left Bundle Branch Block through Computer Model Simulation.

Left bundle branch block (LBBB) is associated with specific septal-to-lateral wall activation patterns which are strongly influenced by the intrinsic left ventricular (LV) contractility and myocardial scar localization. The objective of this study was to propose a computational-model-based interpretation of the different patterns of LV contraction observed in the case of LBBB and preserved contractility or myocardial scarring. Two-dimensional transthoracic echocardiography was used to obtain LV volumes and deformation patterns in three patients with LBBB: (1) a patient with non-ischemic dilated cardiomyopathy, (2) a patient with antero-septal myocardial scar, and (3) a patient with lateral myocardial scar. Scar was confirmed by the distribution of late gadolinium enhancement with cardiac magnetic resonance imaging (cMRI). Model parameters were evaluated manually to reproduce patient-derived data such as strain curves obtained from echocardiographic apical views. The model was able to reproduce the specific strain patterns observed in patients. A typical septal flash with pre-ejection shortening, rebound stretch, and delayed lateral wall activation was observed in the case of non-ischemic cardiomyopathy. In the case of lateral scar, the contractility of the lateral wall was significantly impaired and septal flash was absent. In the case of septal scar, septal flash and rebound stretch were also present as previously described in the literature. Interestingly, the model was also able to simulate the specific contractile properties of the myocardium, providing an excellent localization of LV scar in ischemic patients. The model was able to simulate the electromechanical delay and specific contractility patterns observed in patients with LBBB of ischemic and non-ischemic etiology. With further improvement and validation, this technique might be a useful tool for the diagnosis and treatment planning of heart failure patients needing CRT.

Cardiac radioablation for ventricular tachycardia: Which approach for incorporating cardiorespiratory motions into the planning target volume?

PURPOSE: To evaluate different approaches for generating a cardiorespiratory ITV for cardiac radioablation. METHODS: Four patients with ventricular tachycardia were included in this study. For each patient, cardiac-gated and respiration-correlated 4D-CT scans were acquired. The cardiorespiratory ITV was defined using registrations of the cardiac and respiratory 4D-CT images. Five different approaches, which differed in the number of incorporated cardiac phases (1, 2, 10, or 1 with a fixed 3 mm margin (FM) expansion) and respiratory phases (2 or 10), were evaluated. For each approach, a VMAT treatment plan was simulated. Target coverage (TC) and spill were evaluated geometrically and dosimetrically for each approach. RESULTS: When employing one cardiac phase, the TC did not exceed 85%. Using the two extreme phases of the cardiac and respiratory cycles resulted in a geometric TC < 88% for two patients, with a dosimetric TC of 83% for one patient. An acceptable TC for all patients (geometric TC > 89%, dosimetric TC > 92%) was only achieved when combining 10 respiratory phases with either 2 or 10 cardiac phases or a single cardiac phase with FM. The use of a single cardiac phase with FM combined with 10 respiratory phases lead to a mean geometric and dosimetric spill of 43% and 35%, respectively. CONCLUSION: For cardiac radioablation, the use of two extreme cardiac phases combined with 10 respiratory phases is a robust approach to generate a cardiorespiratory ITV. The use of a single cardiac phase with or without fixed margin expansion is not recommended based on this study.

Functional age estimation through neonatal motion characterization using continuous video recordings.

The follow-up of the development of the premature baby is a major component of its clinical care since it has been shown that it can reveal a pathology. However, no method allowing an automated and continuous monitoring of this development has been proposed. Within the framework of the Digi-NewB European project, our team wishes to offer new clinical indices qualifying the maturation of newborns. In this study, we propose a new method to characterize motor activity from video recordings. For this purpose, we have chosen to characterize the motion temporal organization by drawing inspiration from sleep organization. Thus, we propose a fully automatic process allowing to extract motion features and to combine them to estimate a functional age. By investigating two datasets, one of 28.5 hours (manually annotated) from 33 newborns and one of 4,920 hours from 46 newborns, we show that the proposed approach is relevant for monitoring in clinical routine and that the extracted features reflect the maturation of preterm newborns. Indeed, a compact and interpretable model using gestational age and three motion features (mean duration of intervals with motion, total percentage of time spent in motion and number of intervals without motion) was designed to predict post-menstrual age of newborns and showed an admissible mean absolute error of 1.3 weeks. While the temporal organization of motion was not studied clinically due to a lack of technological means, these results open the door to new developments, new investigations and new knowledge on the evolution of motion in newborns.

[Atrial fibrillation in three cardiological reference centers in Dakar: Senegal data from the AFRICA register survey]. / La fibrillation atriale dans trois centres cardiologiques de référence de Dakar: données sénégalaises de l'enquête du registre AFRICA.

Introduction: atrial fibrillation (AF) is the most common cardiac rhythm disorder. Its prevalence is underestimated in Africa, hence the initiation of the Atrial Fibrillation Registry In Countries of Africa (AFRICA). The aim of our study was to describe, within the framework of the AFRICA registry, the epidemiological, clinical, paraclinical, therapeutic and evolutionary aspects of atrial fibrillation (AF) in Africa, particularly in Senegal. Methods: we performed a cross-sectional, retrospective, multicentric study conducted from January 1st to December 31st 2017, in three referral cardiology wards in Senegal. Results: one hundred and sixty-eight patients, with a mean age of 63 years, were selected, representing a hospital prevalence of 5.99%. There was a predominance of women with sex-ratio of 0.69. High blood pressure was the most frequent risk factor (24.4%). Heart failure was the most frequent circumstance of discovery (59.52%). AF was persistent in 52.24% and valvular AF accounted for 31% and was more frequent in young people (p= 0.005). Left ventricular systolic function was impaired in 55.7%, the left atrium was dilated in 70.83%. The strategy to reduce heart rate was the most used. Patients with CHA2DS2VASC ≥ 2 received anticoagulation with LMWH and oral relay maid mostly of VKA. The complications were dominated by heart failure (66.6%) and ischemic stroke cerebral (28%). Conclusion: atrial fibrillation (AF) is the most frequent cardiac rhythm disorder. It is a major public health concern.

Cardiac stereotactic ablative radiotherapy for refractory ventricular arrhythmias: A radical alternative? A narrative review of rationale and cardiological aspects.

Ventricular arrhythmias are serious life-threatening cardiac disorders. Despite many technological improvements, a non-negligible number of patients present refractory ventricular tachycardias, resistant to a catheter ablation procedure, placing these patients in a therapeutic impasse. Recently, a cardiac stereotactic radioablative technique has been developed to treat patients with refractory ventricular arrhythmias, as a bail out strategy. This new therapeutic option historically brings together two fields of expertise unknown to each other, pointing out the necessity of an optimal partnership between cardiologists and radiation oncologists. As described in this narrative review, the understanding of cardiological aspects of the technique for radiation oncologists and treatment technical aspects comprehension for cardiologists represent a major challenge for the application and the future development of this promising treatment.

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning.

Video-based motion analysis recently appeared to be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and noninvasive. However it is important to remove periods when the newborn is absent or an adult is present from the analysis. In this paper, we propose a method for automatic detection of preterm newborn presence in incubator and open bed. We learn a specific model for each bed type as the camera placement differs a lot and the encountered situations are different between both. We break the problem down into two binary classifications based on deep transfer learning that are fused afterwards: newborn presence detection on the one hand and adult presence detection on the other hand. Moreover, we adopt a strategy of decision intervals fusion in order to take advantage of temporal consistency. We test three deep neural network that were pre-trained on ImageNet: VGG16, MobileNetV2 and InceptionV3. Two classifiers are compared: support vector machine and a small neural network. Our experiments are conducted on a database of 120 newborns. The whole method is evaluated on a subset of 25 newborns including 66 days of video recordings. In incubator, we reach a balanced accuracy of 86%. In open bed, the performance is lower because of a much wider variety of situations whereas less data are available.

Corrigendum: Spjut R, Simon A, Guissard M, Magain N, Sérusiaux E (2020) The fruticose genera in the Ramalinaceae (Ascomycota, Lecanoromycetes): their diversity and evolutionary history. MycoKeys 73: 1-68. https://doi.org/10.3897/mycokeys.73.47287.

[This corrects the article DOI: 10.3897/mycokeys.73.47287.].