A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules

Background: Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk. Methods: 502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores. Findings: 499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77–0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70–0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80–0.93) compared to 0.67 (95% CI 0.55–0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75–0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63–0.85). 18 out of 22 (82%) malignant nodules in the Herder 10–70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention. Interpretation: The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. Funding This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316)

Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing the number of training samples, but these techniques often produce limited and unconvincing results. To address this issue, a growing number of studies have proposed the use of deep generative models to generate more realistic and diverse data that conform to the true distribution of the data. In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion models. We provide an overview of the current state of the art in each of these models and discuss their potential for use in different downstream tasks in medical imaging, including classification, segmentation, and cross-modal translation. We also evaluate the strengths and limitations of each model and suggest directions for future research in this field. Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis

Clasificación de cáncer de pulmón en imágenes de tomografías mediante procesamiento de imágenes y aprendizaje automático

La detección de cáncer de pulmón puede resultar complicada para los profesionales de la salud en sus primeras etapas, ya que es difícil identificarlo a partir de imágenes médicas, lo que supone un obstáculo para comenzar un tratamiento adecuado para los pacientes. Esta enfermedad es la principal causa de muerte, con un incremento de nuevos casos, fallecimientos y cada año mueren más personas por este cáncer que por cáncer de mama, próstata y colon. Las técnicas de clasificación tradicionales tienden a no mejorar sus métricas de evaluación debido a sus procesos de filtrado, segmentación, extracción de características y clasificación. La detección tradicional requiere una gran cantidad de tiempo y recursos económicos. La metodología consta de seis pasos: se inicia con una investigación previa para revisar diferentes estudios. Luego, se selecciona un conjunto de datos. En la tercera etapa se eligen las arquitecturas más destacadas para clasificar con relación al conjunto de datos ImageNet. La cuarta etapa se configuran los modelos para entrenamiento y validación. La quinta etapa se evalúa el consumo de recursos y rendimiento de los modelos. Finalmente, se crea una aplicación web que emplea la arquitectura con los mejores resultados. Después de analizar las arquitecturas seleccionadas se obtuvo métricas porcentuales de 97% o más. Sin embargo, las pruebas revelaron que las métricas de exactitud y precisión alcanzaron porcentajes de 95% y 91%, respectivamente. En conclusión, Efficientb4_DA logra mejores resultados alcanzando una exactitud de 95.32%, una precisión de 91.29%, una sensibilidad de 89.84% y una puntuación F de 90.54%.TesisInfraestructura, Tecnología y Medio Ambient

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Knowledge discovery on the integrative analysis of electrical and mechanical dyssynchrony to improve cardiac resynchronization therapy

Cardiac resynchronization therapy (CRT) is a standard method of treating heart failure by coordinating the function of the left and right ventricles. However, up to 40% of CRT recipients do not experience clinical symptoms or cardiac function improvements. The main reasons for CRT non-response include: (1) suboptimal patient selection based on electrical dyssynchrony measured by electrocardiogram (ECG) in current guidelines; (2) mechanical dyssynchrony has been shown to be effective but has not been fully explored; and (3) inappropriate placement of the CRT left ventricular (LV) lead in a significant number of patients. In terms of mechanical dyssynchrony, we utilize an autoencoder to extract new predictive features from nuclear medicine images, characterizing local mechanical dyssynchrony and improving the CRT response rate. Although machine learning can identify complex patterns and make accurate predictions from large datasets, the low interpretability of these black box methods makes it difficult to integrate them with clinical decisions made by physicians in the healthcare setting. Therefore, we use visualization techniques to enable physicians to understand the physical meaning of new features and the reasoning behind the clinical decisions made by the artificial intelligent model. For electrical dyssynchrony, we use short-time Fourier transform (STFT) to transform one-dimensional waveforms into two-dimensional frequency-time spectra. And transfer learning is used to leverage the knowledge learned from a large arrhythmia ECG dataset of related medical conditions to improve patient selection for CRT with limited data. This improves prediction accuracy, reduces the time and resources required, and potentially leads to better patient outcomes. Furthermore, an innovative approach is proposed for using three-dimensional spatial VCG information to describe the characteristics of electrical dyssynchrony, locate the latest activation site, and combine it with the latest mechanical contraction site to select the optimal LV lead position. In addition, we apply deep reinforcement learning to the decision-making problem of CRT patients. We investigate discrete state space/specific action space models to find the best treatment strategy, improve the reward equation based on the physician\u27s experience, and learn the approximation of the best action-value function that can improve the treatment policy used by clinicians and provide interpretability

Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practice

Neuroimaging offers an unmatched description of the brain’s structure and physiology, but the information it provides is not easy to extract and interpret. A popular way to extract meaningful information from brain images is to use computational methods based on machine learning and deep learning to predict the current or future diagnosis of a patient. A large number of these approaches have been dedicated to the computer-aided diagnosis of dementia, and more specifically of Alzheimer's disease. However, only a few are translated to the clinic. This can be explained by different factors such as the lack of rigorous validation of these approaches leading to over-optimistic performance and their lack of reproducibility, but also the limited interpretability of these methods and their limited generalisability when moving from highly controlled research data to routine clinical data. This manuscript describes how we tried to address these limitations.We have proposed reproducible frameworks for the evaluation of Alzheimer's disease classification methods and developed two open-source software platforms for clinical neuroimaging studies (Clinica) and neuroimaging processing with deep learning (ClinicaDL). We have implemented and assessed the robustness of a visualisation method aiming to interpret convolutional neural networks and used it to study the stability of the network training. We concluded that, currently, combining a convolutional neural networks classifier with an interpretability method may not constitute a robust tool for individual computer-aided diagnosis. As an alternative, we have proposed an approach that detects anomalies in the brain by generating what would be the healthy version of a patient's image and comparing this healthy version with the real image. Finally, we have studied the performance of machine and deep learning algorithms for the computer-aided diagnosis of dementia from images acquired in clinical routine.La neuro-imagerie offre une description inégalée de la structure et de la physiologie du cerveau, mais les informations qu'elle fournit ne sont pas faciles à extraire et à interpréter. Une façon populaire d'extraire des informations pertinentes d'images cérébrales consiste à utiliser des méthodes basées sur l'apprentissage statistique et l'apprentissage profond pour prédire le diagnostic actuel ou futur d'un patient. Un grand nombre de ces approches ont été dédiées au diagnostic assisté par ordinateur de la démence, et plus spécifiquement de la maladie d'Alzheimer. Cependant, seules quelques-unes sont transposées en clinique. Cela peut s'expliquer par différents facteurs tels que l'absence de validation rigoureuse de ces approches conduisant à des performances trop optimistes et à leur manque de reproductibilité, mais aussi l'interprétabilité limitée de ces méthodes et leur généralisation limitée lors du passage de données de recherche hautement contrôlées à des données cliniques de routine. Ce manuscrit décrit comment nous avons tenté de remédier à ces limites.Nous avons proposé des cadres reproductibles pour l'évaluation des méthodes de classification de la maladie d'Alzheimer et développé deux plateformes logicielles open-source pour les études de neuroimagerie clinique (Clinica) et le traitement de la neuroimagerie par apprentissage profond (ClinicaDL). Nous avons implémenté et évalué la robustesse d'une méthode de visualisation visant à interpréter les réseaux neuronaux convolutifs et l'avons utilisée pour étudier la stabilité de l'entraînement du réseau. Nous avons conclu qu'actuellement, la combinaison de réseaux neuronaux convolutifs avec une méthode d'interprétabilité peut ne pas constituer un outil robuste pour le diagnostic individuel assisté par ordinateur. De façon alternative, nous avons proposé une approche qui détecte les anomalies dans le cerveau en générant ce qui serait la version saine de l'image d'un patient et en comparant cette version saine avec l'image réelle. Enfin, nous avons étudié les performances des algorithmes d'apprentissage statistique et profond pour le diagnostic assisté par ordinateur de la démence à partir d'images acquises en routine clinique

Outcomes of MR-guided Stereotactic Body Radiotherapy (SBRT) or yttrium-90 Transarterial Radioembolization for Hepatocellular Carcinoma Treated at an Urban Liver Transplant Center

Background: There are overlapping indications for both stereotactic body radiotherapy (SBRT) and yttrium-90 (Y90) trans-arterial radioembolization as locoregional treatments for hepatocellular cancer, though most centers preferentially use one modality over the other. MR-guided radiation allows both effective on-table localization and integrated motion management as compared with many traditional linear accelerators, allowing SBRT to be done more easily. Y90 radioembolization has been a well-established modality to deliver highly conformal dose due to the localization of the microspheres to the vascular supply of a tumor. We looked at patient characteristics and treatment outcomes for patients receiving MR-guided SBRT or Y90 at an urban transplant center. Objectives: To compare patient characteristics and treatment outcomes of MR-guided SBRT with Y90 transarterial radioembolization in a liver transplant center. Methods: This retrospective single-institution study analyzed patients with HCC treated with SBRT or Y90 from August 2017 to September 2020. To select a patient population eligible for either treatment modality, any Y90 procedures for lesions \u3e 10 cm or for treatment volumes \u3e 1000 cc were omitted from the cohort. A total of 239 patients were included in the analysis, receiving a total of 98 courses of SBRT and 187 courses of Y90 treatment. Local control (LC), freedom from liver progression (FFLP), and overall survival (OS) rates were measured from treatment completion date to death date or last follow-up. All outcomes were censored at time of loss to follow-up; LC and FFLP were censored at time of liver transplant if applicable. Cox regression models were used for survival, with significant factors on the univariate analysis further analyzed with a multivariate model. Results: Median time to follow-up was 11 months (0-44 mo). The mean size of lesions treated with SBRT were smaller than those treated with Y90 (2.7 cm vs 4.3 cm, P\u3c0.01). The groups of patients differed in liver disease characteristics, with SBRT patients having fewer Child-Pugh A disease (62% vs 80%, P\u3c0.01), more having received locoregional treatments to the liver in the past (81% v 35%, P\u3c0.01), and more disease in previously treated liver (57% vs 25%, P\u3c0.01). Dose of radiation for SBRT was 45-50 Gy administered in 5 fractions; dose of Y90 radiation to tumor was prescribed to a median of 235.2 Gy (range 55.8-512.3 Gy). There was a higher rate of one year LC in the SBRT cohort (77% vs 57%, P\u3c0.01), while median FFLP (9 mo vs 8 mo, P=NS) and median OS were not significantly different (24 mo vs 21 mo, P=NS). Multivariate analysis revealed size of largest lesion (P\u3c0.01) was correlated with decreased local control; a 1 cm increase in tumor size was associated with a 25% increased risk of local failure. Subsequent transplant (P\u3c0.01) was the remaining significant factor. Treatment modality did not remain an independent predictor of LC. Predictors of OS in multivariate analysis included age (P=0.01), prior liver treatments (HR 2.86, P\u3c0.01), size of largest lesion (P\u3c0.01), Child-Pugh stage (P\u3c0.01), portal vein thrombosis (HR 1.6, P=0.04), and subsequent liver transplant (HR 0.08, P\u3c0.01). Conclusions: These findings support the effectiveness of both MR-guided SBRT and Y90 transarterial radioembolization in locoregional management of HCC at a single institution despite clear differences in the patient cohorts. Though survival outcomes were comparable, local control differences favored the cohort treated by SBRT, in large part due to differences in tumor size. This data supports further investigation in a randomized study between SBRT and Y90

Racial Differences in Treatments and Toxicity in Non-Small Cell Lung Cancer Patients Treated with Thoracic Radiation Therapy

Background: Racial disparities are of particular concern for lung cancer patients given historical differences in surgery rates for African-American lung cancer patients that resulted in lower overall survival and higher recurrence rates compared with rates in White patients. Objectives: The overall objective of this study was to examine racial differences in thoracic radiation therapy (RT) treatments and toxicities in a large cohort of patients from a multi-institutional consortium database of non-small cell lung cancer (NSCLC) patients. Methods: A large multi-institutional statewide prospectively collected patient-level database of locally advanced (stage II or III) NSCLC patients who received thoracic RT from March 2012 to November 2019 was analyzed to assess the associations between race and treatment and toxicity variables. Race (White or African-American) was defined by patient self-report or if not available then by the electronic medical record system classification. Race categories other than White or African-American comprised a small minority of patients and were excluded from this analysis. Patient-reported toxicity was determined by validated tools including the Functional Assessment of Cancer Therapy-Lung (FACT-L) quality of life instrument. Provider-reported toxicity was determined by the Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. Uni-variable and multi-variable regression models were then fitted to assess relationships between primary outcomes by race and indicators of high-quality treatment and secondary analysis of symptoms. Spearman rank correlation coefficients were calculated between provider reported toxicity and similar patient reported outcomes for each race category. Results: A total of 1441 patients from 24 institutions with mean age of 68 years (range 38-94) were evaluated; 226 patients were African-American, of whom 61% were treated at three facilities. Race was not significantly associated with RT treatment approach, use of concurrent chemotherapy, or the dose to the planning target volume (PTV) or organs at risk including the heart and lungs. However, there was increased patient-reported general pain in African-American patients (compared with White patients) at several time points including pre-RT (22% (vs 15%), P=0.02) and at the end of RT (30% (vs 17%), P=0.001). African-American patients were significantly less likely to have provider-reported grade 2+ radiation pneumonitis (odds ratio (OR) 0.36, P=0.03), despite similar levels of patient-reported respiratory toxicities such as cough and shortness of breath and even after controlling for known patient and treatment-related factors. Correlation coefficients between provider- and patient-reported toxicities were generally similar across race categories. Conclusions: In this large multi-institutional observational study, we reassuringly found no evidence of differences in radiation treatment or chemotherapy approaches by race, in contrast to historical differences by race in surgical care that led to worse survival and outcomes in minority race patients. However, we did unexpectedly find that African-American race was associated with lower odds of provider-reported grade 2+ radiation pneumonitis despite similar patient-reported toxicities of shortness of breath and cough. There are several possibilities for this finding including that pneumonitis is a multifactorial diagnosis that relies on clinical as well as radiologic information and clinical information alone may be insufficient. The Spearman correlation analysis also revealed stronger correlations between patient- and provider-reported toxicities in White patients compared with African-American patients, particularly for trouble swallowing/esophagitis. These findings together for pneumonitis and esophagitis discouragingly suggest possible under-recognition of symptoms in black patients. Further investigation is now warranted to better understand how these findings impact the care of racially diverse lung cancer patients

Survival Outcomes and Patterns of Recurrence After Adjuvant Vaginal Cuff Brachytherapy and Chemotherapy in Early-Stage Uterine Serous Carcinoma

Background: Uterine serous carcinoma (USC) is a relatively rare histology that portends a poor prognosis. The optimal adjuvant therapy for early-stage USC remains controversial; however, adjuvant vaginal cuff brachytherapy (VB) and chemotherapy is a commonly utilized strategy. Objectives: We sought to characterize predictors of survival endpoints and determine recurrence patterns in women with early-stage USC who received adjuvant VB and chemotherapy. Methods: We queried our prospectively maintained database for patients with 2009 FIGO stages I-II USC who underwent adequate surgical staging at our institution and received adjuvant chemotherapy with carboplatin and paclitaxel along with VB. We excluded women with synchronous malignancies. Overall survival (OS), disease-specific survival (DSS), and recurrence-free survival (RFS) were assessed by Kaplan-Meier and log-rank tests. Univariate (UVA) and multivariate analyses (MVA) were performed to identify statistically significant predictors of survival endpoints. Variables with P\u3c0.1 on UVA were included in a MVA and any variable with P\u3c0.05 was considered statistically significant. Results: We identified 77 women who met our inclusion criteria who underwent surgical staging between 1991 and 2018. The median follow-up time was 36 months (range 6-125). The median age was 66 years. Of the cohort, 70% were FIGO stage IA, 17% were stage IB, and 13% were stage II. The median number of dissected lymph nodes (LN) was 22. There were 10 women (13%) diagnosed with a recurrence with a median time to recurrence of 12.0 months. The main site of initial recurrence was distant in seven patients (70%) with the remaining recurrences being pelvic/para-aortic. The 5-year RFS for patients who experienced a distant recurrence was 87% (95% Confidence Interval [CI] 0.75-0.94). For the entire cohort, 5-year OS, DSS, and RFS were 83% (95% CI 0.68-0.91), 92% (95% CI 0.78-0.97), and 83% (95% CI 0.71-0.91), respectively. The sole predictor of 5-year OS on UVA was receipt of omentectomy (P=0.09). The predictors of 5-year DSS on UVA were presence of positive peritoneal cytology (P=0.03), number of LN examined (Hazard Ratio [HR] 1.10, 95% CI 1.00-1.21, P=0.05), and number of para-aortic LN examined (HR 1.16 [95% CI 1.01-1.32], P=0.03). The sole independent predictor of DSS was the presence of positive peritoneal cytology (HR 0.03 [95% CI 0.00-0.72], P=0.03). Predictors of five-year RFS on UVA were robotic vs open surgery technique (P=0.06), presence of positive peritoneal cytology (P=0.01), percent myometrial invasion (HR 5.59 [95% CI 0.84-37.46], P=0.08), and presence of lymphovascular space invasion (LVSI) (P=0.05). Conclusions: Five-year survival outcomes were promising in this cohort of women with early-stage USC treated with adjuvant chemotherapy and VB; however, this study shows that the predominant pattern of relapse in this population is distant, suggesting the need to optimize systemic therapy. Possible predictors of worse outcomes include positive peritoneal cytology, deep myometrial invasion, and presence of LVSI. Multi-institutional pooled analyses are warranted to confirm our study results

Evaluating Quality of Life and Functional Outcomes in Salvage Surgery for Head and Neck Cancer

Background: Unique challenges surround treatment for residual or recurrent head and neck squamous cell carcinoma (HNSCC). Of the limited treatment options for residual or recurrent HNSCC, salvage surgery is often the best option. However, salvage surgery can result in significant morbidity, affecting both quality of life (QoL) and functional outcomes. Few studies have examined QoL outcomes following salvage surgery in the setting of HNSCC. Objectives: To analyze head and neck related quality of life and functional outcomes in patients with head and neck cancer who underwent salvage surgery. Methods: In this IRB approved study, FACT-HN Version 4 was administered pre-operatively and 6 months post-operatively to patients undergoing salvage surgery for HNSCC between November 4, 2014 and April 27, 2020. Retrospective cohort analysis was performed on this population with major outcome being postoperative QoL score. Functional outcomes included postoperative tracheostomy and feeding tube status. QoL outcomes were compared with paired t-tests. Univariate logistic regression was used to determine characteristics associated with presence of permanent tracheostomy and feeding tube, defined as presence greater than 30 days. Results: Overall, 25 patients undergoing salvage surgery for HNSCC were included in this analysis. Primary tumor sites were larynx/hypopharynx (44.0%), oral cavity (24.0%), oropharynx (20.0%), salivary (4.0%), skin (4.0%), and unknown primary (4.0%). Salvage surgeries consisted of total laryngectomy (36.0%), definitive neck dissection (24.0%), mandibulectomy (16.0%), parotidectomy (8.0%), with total laryngectomy/total glossectomy, radical tonsillectomy, TORS base of tongue excision, and transoral laser laryngeal excision all comprising 4% of cases. Total QoL scores were not significantly different preoperatively to postoperatively (mean 108.7, 95% CI=97.7 to 119.7 vs. 103.8, 95% CI: 93.1 to 114.5; P=0.436, with maximum total score of 148). Patients with lower preoperative Emotional Well-Being (EWB) subscores demonstrated significantly worse EWB subscores postoperatively (postoperative mean: 17.0, 95% CI: 14.5 to 19.4 vs. 21.7, 95% CI: 20.0 to 23.4; P=0.002). Of patients who underwent tracheostomy tube placement, 53.8% (N=7/13) remained tracheostomy dependent long-term (\u3e30 d). Of patients who underwent feeding tube placement, 81.0% (N=17/21) remained feeding tube dependent long-term (\u3e30 d). Tracheostomy and feeding tubes remained in place with median durations of 3.02 months (range 0.16 to 20.55) and 10.13 months (range 0 to 24.89), respectively. All patients with T3/4 disease undergoing salvage surgery required long-term feeding tube (N=6). Conclusions: This study provides important information about quality of life and functional outcomes for patients undergoing salvage surgery for HNSCC. There is a high rate of long-term tracheostomy and feeding tube dependence following salvage surgery. While no difference was found in head and neck related quality of life total score and sub-scores at 6 months postoperatively, general emotional well-being preoperatively was most associated with general emotional well-being postoperatively. This information should be taken into consideration when counseling and managing patients with residual or recurrent HNSCC