Auto-Segmentation of Target Volume and Organs-at-risks for Radiotherapy in Breast Cancer patients

목적: 유방암 방사선 치료에서 치료 체적에 대한 정확한 타겟 그리기는 중요하다. 하지만 방사선 치료 계획 과정에 타겟 그리기는 의료진의 부담을 주고 있으며, 의료진 간의 편차는 존재하고 있다. 본 연구에서는 Deep learning-based auto-segmentation (DLBAS)의 성능을 atlas-based segmentation solutions (ABAS)와 비교하고, 임상 의사의 관점에서 유용성을 평가하고, 최종적으로 외부 타당도 조사를 통하여 유방암 방사선 치료에서 자동 구획화의 가능성을 규명하고자 한다. 대상 및 방법: 유방암 방사선 치료 체적과 정상장기들에 대하여 한 명의 연구진에 의하여 구획화 정보를 생성하였다. Convolutional neural network 알고리즘을 이용하여 auto-contours를 생성하였고, Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD)를 이용하여 ABAS와 비교하였다. DLBAS에 의해 생성된 auto-contours의 질적인 평가를 조사하였고, manual contours와 방사선 치료 선량-체적 히스토그램을 비교하여 주요 선량평가분석을 시행하였다. 마지막으로 2개 기관의 11명의 전문가에게 manual contour를 그릴 것을 요청하여 데이터를 수집하였다. 외부 위원회를 통해 가장 최적의 치료 체적을 선정하였고, 나머지 10명의 contour와 DLBAS에 의해 생성된 auto-contour의 성능을 비교하여 순위 평가를 시행하였다. 결과: 제안된 DLBAS 모델은 대부분의 체적 (특히, 치료 체적과 심장 세부구조)에서 ABAS보다 더 일관된 결과와 높은 DSC와 낮은 HD 결과 값을 보였다. ABAS는 연조직의 정상장기와 조영제를 쓰지 않은 새로운 데이터 셋에서 DLBAS에 비해, 제한적인 성능을 보였다. 질적 평가를 위한 설문조사가 시행되었고, 중위수 8점으로 manual contour와 auto-contour 사이의 차이가 크지 않다고 대답하였으며, 임상에서 도움이 될 것으로 답변하였다. 또한 선량평가 분석 결과에서 차이는 미미하였다. 외부 검증 결과, 9개의 정상장기를 그리는데 평균 37분이 걸렸고, DLBAS는 6분이 걸렸다. Auto-contour는 전체 12개 중 1위 manual contour와 비교하였을 때 가장 DSC상 차이가 적었으며, HSD상 2번째로 차이가 적었다. 정상장기에서 가장 편차가 높았던 부위는 유방이었다. 결론: 유방 방사선 치료 계획에서 DLBAS의 실현가능성은 이번 연구에서 다각도로 검증되었다. 의료진의 최종 수정 과정은 필수적이지만, 앞으로 DLBAS는 방사선 치료를 도울 수 있는 훌륭한 가능성을 보여주었다.open박

Contribuciones de las técnicas machine learning a la cardiología. Predicción de reestenosis tras implante de stent coronario

[ES]Antecedentes: Existen pocos temas de actualidad equiparables a la posibilidad de la tecnología actual para desarrollar las mismas capacidades que el ser humano, incluso en medicina. Esta capacidad de simular los procesos de inteligencia humana por parte de máquinas o sistemas informáticos es lo que conocemos hoy en día como inteligencia artificial. Uno de los campos de la inteligencia artificial con mayor aplicación a día de hoy en medicina es el de la predicción, recomendación o diagnóstico, donde se aplican las técnicas machine learning. Asimismo, existe un creciente interés en las técnicas de medicina de precisión, donde las técnicas machine learning pueden ofrecer atención médica individualizada a cada paciente. El intervencionismo coronario percutáneo (ICP) con stent se ha convertido en una práctica habitual en la revascularización de los vasos coronarios con enfermedad aterosclerótica obstructiva significativa. El ICP es asimismo patrón oro de tratamiento en pacientes con infarto agudo de miocardio; reduciendo las tasas de muerte e isquemia recurrente en comparación con el tratamiento médico. El éxito a largo plazo del procedimiento está limitado por la reestenosis del stent, un proceso patológico que provoca un estrechamiento arterial recurrente en el sitio de la ICP. Identificar qué pacientes harán reestenosis es un desafío clínico importante; ya que puede manifestarse como un nuevo infarto agudo de miocardio o forzar una nueva resvascularización del vaso afectado, y que en casos de reestenosis recurrente representa un reto terapéutico. Objetivos: Después de realizar una revisión de las técnicas de inteligencia artificial aplicadas a la medicina y con mayor profundidad, de las técnicas machine learning aplicadas a la cardiología, el objetivo principal de esta tesis doctoral ha sido desarrollar un modelo machine learning para predecir la aparición de reestenosis en pacientes con infarto agudo de miocardio sometidos a ICP con implante de un stent. Asimismo, han sido objetivos secundarios comparar el modelo desarrollado con machine learning con los scores clásicos de riesgo de reestenosis utilizados hasta la fecha; y desarrollar un software que permita trasladar esta contribución a la práctica clínica diaria de forma sencilla. Para desarrollar un modelo fácilmente aplicable, realizamos nuestras predicciones sin variables adicionales a las obtenidas en la práctica rutinaria. Material: El conjunto de datos, obtenido del ensayo GRACIA-3, consistió en 263 pacientes con características demográficas, clínicas y angiográficas; 23 de ellos presentaron reestenosis a los 12 meses después de la implantación del stent. Todos los desarrollos llevados a cabo se han hecho en Python y se ha utilizado computación en la nube, en concreto AWS (Amazon Web Services). Metodología: Se ha utilizado una metodología para trabajar con conjuntos de datos pequeños y no balanceados, siendo importante el esquema de validación cruzada anidada utilizado, así como la utilización de las curvas PR (precision-recall, exhaustividad-sensibilidad), además de las curvas ROC, para la interpretación de los modelos. Se han entrenado los algoritmos más habituales en la literatura para elegir el que mejor comportamiento ha presentado. Resultados: El modelo con mejores resultados ha sido el desarrollado con un clasificador extremely randomized trees; que superó significativamente (0,77; área bajo la curva ROC a los tres scores clínicos clásicos; PRESTO-1 (0,58), PRESTO-2 (0,58) y TLR (0,62). Las curvas exhaustividad sensibilidad ofrecieron una imagen más precisa del rendimiento del modelo extremely randomized trees que muestra un algoritmo eficiente (0,96) para no reestenosis, con alta exhaustividad y alta sensibilidad. Para un umbral considerado óptimo, de 1,000 pacientes sometidos a implante de stent, nuestro modelo machine learning predeciría correctamente 181 (18%) más casos en comparación con el mejor score de riesgo clásico (TLR). Las variables más importantes clasificadas según su contribución a las predicciones fueron diabetes, enfermedad coronaria en 2 ó más vasos, flujo TIMI post-ICP, plaquetas anormales, trombo post-ICP y colesterol anormal. Finalmente, se ha desarrollado una calculadora para trasladar el modelo a la práctica clínica. La calculadora permite estimar el riesgo individual de cada paciente y situarlo en una zona de riesgo, facilitando la toma de decisión al médico en cuanto al seguimiento adecuado para el mismo. Conclusiones: Aplicado inmediatamente después de la implantación del stent, un modelo machine learning diferencia mejor a aquellos pacientes que presentarán o no reestenosis respecto a los discriminadores clásicos actuales

Artifact Correction and Real-Time Scatter Estimation for X-Ray Computed Tomography in Industrial Metrology

Artifacts often limit the application of computed tomography (CT) in industrial metrology. In order to correct these artifacts, the so-called simulation-based artifact correction (SBAC) was developed in this thesis. For this purpose, analytical and Monte Carlo (MC) based models were set up to simulate the CT measurement process for a given component as accurately and efficiently as possible. Calculating the difference between this simulation and an ideal one yields an estimate of the present artifacts that can be used to correct the corresponding CT measurement. The potential of this approach was demonstrated for the correction of the most common CT artifacts, i.e. beam hardening, x-ray scattering, off-focal radiation, partial volume effects, and cone-beam artifacts. In any case, the SBAC provided CT reconstructions that showed almost no artifacts and whose quality was clearly superior to state-of-the-art reference approaches. In this context, the problem of long runtimes of scatter simulations was solved by another novel approach, the so-called deep scatter estimation (DSE). The DSE uses a deep convolutional neural network which was trained to map the acquired projection data to given MC scatter estimates. Once the DSE network is trained, it can be used to process unknown data in real-time. In different simulation studies and measurements, it could be shown that DSE generalizes to various acquisition conditions and components while providing scatter distributions that differ by less than 2 % from MC simulations. Thus, the two developed approaches make an important contribution to correct CT artifacts efficiently and to extend the applicability of CT in the field of industrial metrology

Development of deep learning methods for head and neck cancer detection in hyperspectral imaging and digital pathology for surgical guidance

Surgeons performing routine cancer resections utilize palpation and visual inspection, along with time-consuming microscopic tissue analysis, to ensure removal of cancer. Despite this, inadequate surgical cancer margins are reported for up to 10-20% of head and neck squamous cell carcinoma (SCC) operations. There exists a need for surgical guidance with optical imaging to ensure complete cancer resection in the operating room. The objective of this dissertation is to evaluate hyperspectral imaging (HSI) as a non-contact, label-free optical imaging modality to provide intraoperative diagnostic information. For comparison of different optical methods, autofluorescence, RGB composite images synthesized from HSI, and two fluorescent dyes are also acquired and investigated for head and neck cancer detection. A novel and comprehensive dataset was obtained of 585 excised tissue specimens from 204 patients undergoing routine head and neck cancer surgeries. The first aim was to use SCC tissue specimens to determine the potential of HSI for surgical guidance in the challenging task of head and neck SCC detection. It is hypothesized that HSI could reduce time and provide quantitative cancer predictions. State-of-the-art deep learning algorithms were developed for SCC detection in 102 patients and compared to other optical methods. HSI detected SCC with a median AUC score of 85%, and several anatomical locations demonstrated good SCC detection, such as the larynx, oropharynx, hypopharynx, and nasal cavity. To understand the ability of HSI for SCC detection, the most important spectral features were calculated and correlated with known cancer physiology signals, notably oxygenated and deoxygenated hemoglobin. The second aim was to evaluate HSI for tumor detection in thyroid and salivary glands, and RGB images were synthesized using the spectral response curves of the human eye for comparison. Using deep learning, HSI detected thyroid tumors with 86% average AUC score, which outperformed fluorescent dyes and autofluorescence, but HSI-synthesized RGB imagery performed with 90% AUC score. The last aim was to develop deep learning algorithms for head and neck cancer detection in hundreds of digitized histology slides. Slides containing SCC or thyroid carcinoma can be distinguished from normal slides with 94% and 99% AUC scores, respectively, and SCC and thyroid carcinoma can be localized within whole-slide images with 92% and 95% AUC scores, respectively. In conclusion, the outcomes of this thesis work demonstrate that HSI and deep learning methods could aid surgeons and pathologists in detecting head and neck cancers.Ph.D