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2.
Proc Natl Acad Sci U S A ; 117(9): 4571-4577, 2020 Mar 03.
Artigo em Inglês | MEDLINE | ID: mdl-32071251

RESUMO

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.

3.
Br J Radiol ; 93(1107): 20190879, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31804145

RESUMO

OBJECTIVE: Locally recurrent disease is of increasing concern in (non-)small cell lung cancer [(N)SCLC] patients. Local reirradiation with photons or particles may be of benefit to these patients. In this multicentre in silico trial performed within the Radiation Oncology Collaborative Comparison (ROCOCO) consortium, the doses to the target volumes and organs at risk (OARs) were compared when using several photon and proton techniques in patients with recurrent localised lung cancer scheduled to undergo reirradiation. METHODS: 24 consecutive patients with a second primary (N)SCLC or recurrent disease after curative-intent, standard fractionated radio(chemo)therapy were included in this study. The target volumes and OARs were centrally contoured and distributed to the participating ROCOCO sites. Remaining doses to the OARs were calculated on an individual patient's basis. Treatment planning was performed by the participating site using the clinical treatment planning system and associated beam characteristics. RESULTS: Treatment plans for all modalities (five photon and two proton plans per patient) were available for 22 patients (N = 154 plans). 3D-conformal photon therapy and double-scattered proton therapy delivered significantly lower doses to the target volumes. The highly conformal techniques, i.e., intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), CyberKnife, TomoTherapy and intensity-modulated proton therapy (IMPT), reached the highest doses in the target volumes. Of these, IMPT was able to statistically significantly decrease the radiation doses to the OARs. CONCLUSION: Highly conformal photon and proton beam techniques enable high-dose reirradiation of the target volume. They, however, significantly differ in the dose deposited in the OARs. The therapeutic options, i.e., reirradiation or systemic therapy, need to be carefully weighed and discussed with the patients. ADVANCES IN KNOWLEDGE: Highly conformal photon and proton beam techniques enable high-dose reirradiation of the target volume. In light of the abilities of the various highly conformal techniques to spare specific OARs, the therapeutic options need to be carefully weighed and patients included in the decision-making process.


Assuntos
Carcinoma Pulmonar de Células não Pequenas/radioterapia , Neoplasias Pulmonares/radioterapia , Recidiva Local de Neoplasia/radioterapia , Órgãos em Risco/efeitos da radiação , Fótons/uso terapêutico , Terapia com Prótons/métodos , Reirradiação/métodos , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Conjuntos de Dados como Assunto , Humanos , Neoplasias Pulmonares/diagnóstico por imagem , Recidiva Local de Neoplasia/diagnóstico por imagem , Órgãos em Risco/diagnóstico por imagem , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador/métodos , Radioterapia Conformacional/métodos , Radioterapia de Intensidade Modulada/métodos , Resultado do Tratamento
4.
Neurooncol Adv ; 1(1): vdz011, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31608329

RESUMO

Background: We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes. Methods: Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan-Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset. Results: Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01-16.7, P = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1-3.5, P = .02) and worse OS (HR 2.94, 95% CI 1.47-5.56, P = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and FOXM1 expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone. Conclusions: Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.

5.
Proc Natl Acad Sci U S A ; 116(40): 19887-19893, 2019 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-31527280

RESUMO

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

6.
Phys Med Biol ; 64(13): 135001, 2019 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-31181561

RESUMO

A deeply supervised attention-enabled boosted convolutional neural network (DAB-CNN) is presented as a superior alternative to current state-of-the-art convolutional neural networks (CNNs) for semantic CT segmentation. Spatial attention gates (AGs) were incorporated into a novel 3D cascaded CNN framework to prioritize relevant anatomy and suppress redundancies within the network. Due to the complexity and size of the network, incremental channel boosting was used to decrease memory usage and facilitate model convergence. Deep supervision was used to encourage semantically meaningful deep features and mitigate local minima traps during training. The accuracy of DAB-CNN is compared to seven architectures: a variation of U-Net (UNet), attention-enabled U-Net (A-UNet), boosted U-Net (B-UNet), deeply-supervised U-Net (D-UNet), U-Net with ResNeXt blocks (ResNeXt), life-long learning segmentation CNN (LL-CNN), and deeply supervised attention-enabled U-Net (DA-UNet). The accuracy of each method was assessed based on Dice score compared to manually delineated contours as the gold standard. One hundred and twenty patients who had definitive prostate radiotherapy were used in this study. Training, validation, and testing followed Kaggle competition rules, with 80 patients used for training, 20 patients used for internal validation, and 20 test patients used to report final accuracies. Comparator p -values indicate that DAB-CNN achieved significantly superior Dice scores than all alternative algorithms for the prostate, rectum, and penile bulb. This study demonstrated that attention-enabled boosted convolutional neural networks (CNNs) using deep supervision are capable of achieving superior prediction accuracy compared to current state-of-the-art automatic segmentation methods.

7.
Artigo em Inglês | MEDLINE | ID: mdl-30954520

RESUMO

PURPOSE: Numerous dose and fractionation schedules have been used to treat medically inoperable stage I non-small cell lung cancer (NSCLC) with stereotactic body radiation therapy (SBRT) or stereotactic ablative radiation therapy. We evaluated published experiences with SBRT to determine local control (LC) rates as a function of SBRT dose. METHODS AND MATERIALS: One hundred sixty published articles reporting LC rates after SBRT for stage I NSCLC were identified. Quality of the series was assessed by evaluating the number of patients in the study, homogeneity of the dose regimen, length of follow-up time, and reporting of LC. Clinical data including 1, 2, 3, and 5-year tumor control probabilities for stages T1, T2, and combined T1 and T2 as a function of the biological effective dose were fitted to the linear quadratic, universal survival curve, and regrowth models. RESULTS: Forty-six studies met inclusion criteria. As measured by the goodness of fit χ2/ndf, with ndf as the number of degrees of freedom, none of the models were ideal fits for the data. Of the 3 models, the regrowth model provides the best fit to the clinical data. For the regrowth model, the fitting yielded an α-to-ß ratio of approximately 25 Gy for T1 tumors, 19 Gy for T2 tumors, and 21 Gy for T1 and T2 combined. To achieve the maximal LC rate, the predicted physical dose schemes when prescribed at the periphery of the planning target volume are 43 ± 1 Gy in 3 fractions, 47 ± 1 Gy in 4 fractions, and 50 ± 1 Gy in 5 fractions for combined T1 and T2 tumors. CONCLUSIONS: Early-stage NSCLC is radioresponsive when treated with SBRT or stereotactic ablative radiation therapy. A steep dose-response relationship exists with high rates of durable LC when physical doses of 43-50 Gy are delivered in 3 to 5 fractions.

8.
Radiother Oncol ; 133: 106-112, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30935565

RESUMO

BACKGROUND AND PURPOSE: Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints to limit this toxicity. Using machine learning algorithms, we performed analyses of contributing factors in the development of RP to uncover previously unidentified criteria and elucidate the relative importance of individual factors. MATERIALS AND METHODS: We evaluated 32 clinical features per patient in a cohort of 203 stage II-III LA-NSCLC patients treated with definitive chemoradiation to a median dose of 66.6 Gy in 1.8 Gy daily fractions at our institution from 2008 to 2016. Of this cohort, 17.7% of patients developed grade ≥2 RP. Univariate analysis was performed using trained decision stumps to individually analyze statistically significant predictors of RP and perform feature selection. Applying Random Forest, we performed multivariate analysis to assess the combined performance of important predictors of RP. RESULTS: On univariate analysis, lung V20, lung mean, lung V10 and lung V5 were found to be significant RP predictors with the greatest balance of specificity and sensitivity. On multivariate analysis, Random Forest (AUC = 0.66, p = 0.0005) identified esophagus max (20.5%), lung V20 (16.4%), lung mean (15.7%) and pack-year (14.9%) as the most common primary differentiators of RP. CONCLUSIONS: We highlight Random Forest as an accurate machine learning method to identify known and new predictors of symptomatic RP. Furthermore, this analysis confirms the importance of lung V20, lung mean and pack-year as predictors of RP while also introducing esophagus max as an important RP predictor.


Assuntos
Carcinoma Pulmonar de Células não Pequenas/radioterapia , Neoplasias Pulmonares/radioterapia , Aprendizado de Máquina , Pneumonite por Radiação/etiologia , Idoso , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/patologia , Quimiorradioterapia , Feminino , Humanos , Pulmão/fisiologia , Pulmão/efeitos da radiação , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/patologia , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Valor Preditivo dos Testes , Dosagem Radioterapêutica
9.
Med Phys ; 46(5): 2204-2213, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-30887523

RESUMO

PURPOSE: This study suggests a lifelong learning-based convolutional neural network (LL-CNN) algorithm as a superior alternative to single-task learning approaches for automatic segmentation of head and neck (OARs) organs at risk. METHODS AND MATERIALS: Lifelong learning-based convolutional neural network was trained on twelve head and neck OARs simultaneously using a multitask learning framework. Once the weights of the shared network were established, the final multitask convolutional layer was replaced by a single-task convolutional layer. The single-task transfer learning network was trained on each OAR separately with early stoppage. The accuracy of LL-CNN was assessed based on Dice score and root-mean-square error (RMSE) compared to manually delineated contours set as the gold standard. LL-CNN was compared with 2D-UNet, 3D-UNet, a single-task CNN (ST-CNN), and a pure multitask CNN (MT-CNN). Training, validation, and testing followed Kaggle competition rules, where 160 patients were used for training, 20 were used for internal validation, and 20 in a separate test set were used to report final prediction accuracies. RESULTS: On average contours generated with LL-CNN had higher Dice coefficients and lower RMSE than 2D-UNet, 3D-Unet, ST- CNN, and MT-CNN. LL-CNN required ~72 hrs to train using a distributed learning framework on 2 Nvidia 1080Ti graphics processing units. LL-CNN required 20 s to predict all 12 OARs, which was approximately as fast as the fastest alternative methods with the exception of MT-CNN. CONCLUSIONS: This study demonstrated that for head and neck organs at risk, LL-CNN achieves a prediction accuracy superior to all alternative algorithms.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Órgãos em Risco/diagnóstico por imagem , Carcinoma de Células Escamosas de Cabeça e Pescoço/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Automação , Humanos , Órgãos em Risco/efeitos da radiação , Radioterapia Guiada por Imagem , Risco , Carcinoma de Células Escamosas de Cabeça e Pescoço/radioterapia
10.
Phys Med ; 58: 47-53, 2019 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-30824149

RESUMO

This work presents a systematic approach for testing a dose calculation algorithm over a variety of conditions designed to span the possible range of clinical treatment plans. Using this method, a TrueBeam STx machine with high definition multi-leaf collimators (MLCs) was commissioned in the RayStation treatment planning system (TPS). The initial model parameters values were determined by comparing TPS calculations with standard measured depth dose and profile curves. The MLC leaf offset calibration was determined by comparing measured and calculated field edges utilizing a wide range of MLC retracted and over-travel positions. The radial fluence was adjusted using profiles through both the center and corners of the largest field size, and through measurements of small fields that were located at highly off-axis positions. The flattening filter source was adjusted to improve the TPS agreement for the output of MLC-defined fields with much larger jaw openings. The MLC leaf transmission and leaf end parameters were adjusted to optimize the TPS agreement for highly modulated intensity-modulated radiotherapy (IMRT) plans. The final model was validated for simple open fields, multiple field configurations, the TG 119 C-shape target test, and a battery of clinical IMRT and volumetric-modulated arc therapy (VMAT) plans. The commissioning process detected potential dosimetric errors of over 10% and resulted in a final model that provided in general 3% dosimetric accuracy. This study demonstrates the importance of using a variety of conditions to adjust a beam model and provides an effective framework for achieving high dosimetric accuracy.


Assuntos
Modelos Teóricos , Radiometria , Calibragem , Aceleradores de Partículas , Planejamento da Radioterapia Assistida por Computador , Radioterapia de Intensidade Modulada , Espalhamento de Radiação
12.
Int J Radiat Oncol Biol Phys ; 104(2): 302-315, 2019 06 01.
Artigo em Inglês | MEDLINE | ID: mdl-30711529

RESUMO

Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Neoplasias/diagnóstico por imagem , Radioterapia (Especialidade)/métodos , Sistemas de Apoio a Decisões Clínicas , Genômica , Humanos , Modelos Logísticos , Aprendizado de Máquina , Imagem por Ressonância Magnética , Neoplasias/genética , Neoplasias/terapia , Imagens de Fantasmas , Farmacocinética , Fenótipo , Tomografia por Emissão de Pósitrons , Reprodutibilidade dos Testes , Tomografia Computadorizada por Raios X , Resultado do Tratamento
13.
Phys Med Biol ; 63(23): 235022, 2018 12 04.
Artigo em Inglês | MEDLINE | ID: mdl-30511663

RESUMO

The goal of this study is to demonstrate the feasibility of a novel fully-convolutional volumetric dose prediction neural network (DoseNet) and test its performance on a cohort of prostate stereotactic body radiotherapy (SBRT) patients. DoseNet is suggested as a superior alternative to U-Net and fully connected distance map-based neural networks for non-coplanar SBRT prostate dose prediction. DoseNet utilizes 3D convolutional downsampling with corresponding 3D deconvolutional upsampling to preserve memory while simultaneously increasing the receptive field of the network. DoseNet was implemented on 2 Nvidia 1080 Ti graphics processing units and utilizes a 3 phase learning protocol to help achieve convergence and improve generalization. DoseNet was trained, validated, and tested with 151 patients following Kaggle completion rules. The dosimetric quality of DoseNet was evaluated by comparing the predicted dose distribution with the clinically approved delivered dose distribution in terms of conformity index, heterogeneity index, and various clinically relevant dosimetric parameters. The results indicate that the DoseNet algorithm is a superior alternative to U-Net and fully connected methods for prostate SBRT patients. DoseNet required ~50.1 h to train, and ~0.83 s to make a prediction on a 128 × 128 × 64 voxel image. In conclusion, DoseNet is capable of making accurate volumetric dose predictions for non-coplanar SBRT prostate patients, while simultaneously preserving computational efficiency.


Assuntos
Radiocirurgia/métodos , Planejamento da Radioterapia Assistida por Computador/métodos , Algoritmos , Humanos , Dosagem Radioterapêutica
14.
Oral Oncol ; 87: 111-116, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30527225

RESUMO

Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and neck patients. However, the complexity and novelty of AI algorithms make them susceptible to misuse by researchers and clinicians. Understanding nuances of new technologies could serve to mitigate potential clinical implementation pitfalls. This article is intended to facilitate integration of AI into the radiotherapy clinic by providing an overview of AI algorithms, including support vector machines (SVMs), random forests (RF), gradient boosting (GB), and several variations of deep learning. This document describes current AI algorithms that have been applied to head and neck IMRT planning and identifies rapidly growing branches of AI in industry that have potential applications to head and neck cancer patients receiving IMRT. AI algorithms have great clinical potential if used correctly but can also cause harm if misused, so it is important to raise the level of AI competence within radiation oncology so that the benefits can be realized in a controlled and safe manner.


Assuntos
Neoplasias de Cabeça e Pescoço/radioterapia , Aprendizado de Máquina , Lesões por Radiação/prevenção & controle , Planejamento da Radioterapia Assistida por Computador/métodos , Radioterapia de Intensidade Modulada/métodos , Competência Clínica , Tomada de Decisão Clínica/métodos , Humanos , Lesões por Radiação/etiologia , Radio-Oncologistas , Planejamento da Radioterapia Assistida por Computador/efeitos adversos , Radioterapia de Intensidade Modulada/efeitos adversos
15.
JAMA Oncol ; 4(12): 1742-1748, 2018 12 01.
Artigo em Inglês | MEDLINE | ID: mdl-30347008

RESUMO

Importance: Radiation dermatitis is common and often treated with topical therapy. Patients are typically advised to avoid topical agents for several hours before daily radiotherapy (RT) out of concern that topical agents might increase the radiation dose to the skin. With modern RT's improved skin-sparing properties, this recommendation may be irrelevant. Objective: To assess whether applying either metallic or nonmetallic topical agents before radiation treatment alters the skin dose. Design, Setting, and Participants: A 24-question online survey of patients and clinicians was conducted from January 15, 2015, to March 15, 2017, to determine current practices regarding topical therapy use. In preclinical studies, dosimetric effect of the topical agents was evaluated by delivering 200 monitor units and measuring the dose at the surface and at 2-cm depth in a tissue-equivalent phantom with or without 2 common topical agents: a petroleum-based ointment (Aquaphor, petrolatum 41%) and silver sulfadiazine cream, 1%. Skin doses associated with various photon and electron energies, topical agent thicknesses, and beam incidence were assessed. Whether topical agents altered the skin dose was also evaluated in 24 C57BL/6 mice by using phosphorylated histone (γ-H2AX) immunofluorescent staining and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. Preclinical studies took place at the University of Pennsylvania. Main Outcomes and Measures: Patient and clinician survey responses; surface radiation dose readings in tissue-equivalent phantom; and γ-H2AX and TUNEL intensity measured in mice. Results: The 133 patients surveyed received RT for cancer and had a median (range) age of 60 (18-86) years; 117 (87.9%) were women. One hundred eight clinicians completed the survey with 105 reporting that they were involved in managing patient skin care during RT. One hundred eleven (83.4%) of the patients and 96 (91.4%) of the 105 clinicians received or gave the advice to avoid applying topical agents before RT treatments. Dosimetric measurements showed no difference in the delivered dose at either the surface or a 2-cm depth with or without a 1- to 2-mm application of either topical agent when using en face 6- or 15-megavoltage (MV) photons. The same application of topicals did not alter the surface dose as a function of beam incident angle from 15° to 60°, except for a 6% increase at 60° with the silver sulfadiazine cream. Surface dose for 6- and 15-MV beams were significantly increased with a thicker (≥3-mm) topical application. For 6 MV, the surface dose was 1.05 Gy with a thick layer of petroleum-based ointment and 1.02 Gy for silver sulfadiazine cream vs 0.88 Gy without topical agents. For 15 MV, the doses were 0.70 Gy for a thick layer of petroleum-based ointment and 0.60 Gy for silver sulfadiazine cream vs 0.52 Gy for the controls. With 6- and 9-MeV electrons, there was a 2% to 5% increase in surface dose with the use of the topical agents. There were no dose differences at 2-cm depth. Irradiated skin in mice showed no differences in γ-H2AX-positive foci or in TUNEL staining with or without topical agents of varying thickness. Conclusions and Relevance: Thin or moderately applied topical agents, even if applied just before RT, may have minimal influence on skin dose regardless of beam energy or beam incidence. The findings of this study suggest that applying very thick amounts of a topical agent before RT may increase the surface dose and should be avoided.


Assuntos
Contraindicações de Medicamentos , Fármacos Dermatológicos , Aconselhamento Diretivo , Relações Médico-Paciente , Lesões por Radiação/prevenção & controle , Radioterapia/efeitos adversos , Administração Tópica , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Animais , Atitude Frente a Saúde , Fármacos Dermatológicos/administração & dosagem , Fármacos Dermatológicos/efeitos adversos , Aconselhamento Diretivo/métodos , Aconselhamento Diretivo/normas , Fracionamento da Dose de Radiação , Feminino , Humanos , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Pessoa de Meia-Idade , Imagens de Fantasmas , Pele/efeitos dos fármacos , Pele/patologia , Pele/efeitos da radiação , Inquéritos e Questionários , Adulto Jovem
16.
PLoS One ; 13(9): e0204161, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30235308

RESUMO

BACKGROUND: Meningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes. METHODS AND FINDINGS: We developed a comprehensive database containing information from 235 patients who underwent surgery for 257 meningiomas at a single institution from 1990 to 2015. The median follow-up was 4.3 years, and resection specimens were re-evaluated according to current diagnostic criteria, revealing 128 WHO grade I, 104 grade II and 25 grade III meningiomas. A series of ML algorithms were trained and tuned by nested resampling to create models based on preoperative features, conventional postoperative features, or both. We compared different algorithms' accuracy as well as the unique insights they offered into the data. Machine learning models restricted to preoperative information, such as patient demographics and radiographic features, had similar accuracy for predicting local failure (AUC = 0.74) or overall survival (AUC = 0.68) as models based on meningioma grade and extent of resection (AUC = 0.73 and AUC = 0.72, respectively). Integrated models incorporating all available demographic, clinical, radiographic and pathologic data provided the most accurate estimates (AUC = 0.78 and AUC = 0.74, respectively). From these models, we developed decision trees and nomograms to estimate the risks of local failure or overall survival for meningioma patients. CONCLUSIONS: Clinical information has been historically underutilized in the prediction of meningioma outcomes. Predictive models trained on preoperative clinical data perform comparably to conventional models trained on meningioma grade and extent of resection. Combination of all available information can help stratify meningioma patients more accurately.


Assuntos
Meningioma/cirurgia , Cuidados Pós-Operatórios , Cuidados Pré-Operatórios , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Análise por Conglomerados , Árvores de Decisões , Humanos , Aprendizado de Máquina , Pessoa de Meia-Idade , Nomogramas , Fatores de Tempo , Resultado do Tratamento , Adulto Jovem
17.
Int J Radiat Oncol Biol Phys ; 102(4): 1074-1082, 2018 11 15.
Artigo em Inglês | MEDLINE | ID: mdl-30170101

RESUMO

The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.


Assuntos
Diagnóstico por Imagem/métodos , Neoplasias/diagnóstico por imagem , Aprendizado Profundo , Humanos , Processamento de Imagem Assistida por Computador , Disseminação de Informação
18.
Int J Radiat Oncol Biol Phys ; 102(4): 744-756, 2018 11 15.
Artigo em Inglês | MEDLINE | ID: mdl-30108003

RESUMO

PURPOSE: To investigate the prognostic utility of quantitative 3-dimensional magnetic resonance imaging radiomic analysis for primary pediatric embryonal brain tumors. METHODS AND MATERIALS: Thirty-four pediatric patients with embryonal brain tumor with concurrent preoperative T1-weighted postcontrast (T1PG) and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance images were identified from an institutional database. The median follow-up period was 5.2 years. Radiomic features were extracted from axial T1PG and FLAIR contours using MATLAB, and 15 features were selected for analysis based on qualitative radiographic features with prognostic significance for pediatric embryonal brain tumors. Logistic regression, linear regression, receiver operating characteristic curves, the Harrell C index, and the Somer D index were used to test the relationships between radiomic features and demographic variables, as well as clinical outcomes. RESULTS: Pediatric embryonal brain tumors in older patients had an increased normalized mean tumor intensity (P = .05, T1PG), decreased tumor volume (P = .02, T1PG), and increased markers of heterogeneity (P ≤ .01, T1PG and FLAIR) relative to those in younger patients. We identified 10 quantitative radiomic features that delineated medulloblastoma, pineoblastoma, and supratentorial primitive neuroectodermal tumor, including size and heterogeneity (P ≤ .05, T1PG and FLAIR). Decreased markers of tumor heterogeneity were predictive of neuraxis metastases and trended toward significance (P = .1, FLAIR). Tumors with an increased size (area under the curve = 0.7, FLAIR) and decreased heterogeneity (area under the curve = 0.7, FLAIR) at diagnosis were more likely to recur. CONCLUSIONS: Quantitative radiomic features are associated with pediatric embryonal brain tumor patient age, histology, neuraxis metastases, and recurrence. These data suggest that quantitative 3-dimensional magnetic resonance imaging radiomic analysis has the potential to identify radiomic risk features for pediatric patients with embryonal brain tumors.


Assuntos
Neoplasias Encefálicas/diagnóstico por imagem , Imageamento Tridimensional/métodos , Imagem por Ressonância Magnética/métodos , Neoplasias Embrionárias de Células Germinativas/diagnóstico por imagem , Adolescente , Neoplasias Encefálicas/mortalidade , Neoplasias Encefálicas/patologia , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Meduloblastoma/diagnóstico por imagem , Meduloblastoma/patologia , Metástase Neoplásica , Recidiva Local de Neoplasia , Neoplasias Embrionárias de Células Germinativas/mortalidade , Neoplasias Embrionárias de Células Germinativas/patologia , Tumores Neuroectodérmicos Primitivos/diagnóstico por imagem , Tumores Neuroectodérmicos Primitivos/patologia , Pinealoma/diagnóstico por imagem , Pinealoma/patologia , Estudos Retrospectivos
19.
Phys Med Biol ; 63(18): 185017, 2018 09 17.
Artigo em Inglês | MEDLINE | ID: mdl-30109996

RESUMO

The purpose of the work is to develop a deep unsupervised learning strategy for cone-beam CT (CBCT) to CT deformable image registration (DIR). This technique uses a deep convolutional inverse graphics network (DCIGN) based DIR algorithm implemented on 2 Nvidia 1080 Ti graphics processing units. The model is comprised of an encoding and decoding stage. The fully-convolutional encoding stage learns hierarchical features and simultaneously forms an information bottleneck, while the decoding stage restores the original dimensionality of the input image. Activations from the encoding stage are used as the input channels to a sparse DIR algorithm. DCIGN was trained using a distributive learning-based convolutional neural network architecture and used 285 head and neck patients to train, validate, and test the algorithm. The accuracy of the DCIGN algorithm was evaluated on 100 synthetic cases and 12 hold out test patient cases. The results indicate that DCIGN performed better than rigid registration, intensity corrected Demons, and landmark-guided deformable image registration for all evaluation metrics. DCIGN required ~14 h to train, and ~3.5 s to make a prediction on a 512 × 512 × 120 voxel image. In conclusion, DCIGN is able to maintain high accuracy in the presence of CBCT noise contamination, while simultaneously preserving high computational efficiency.


Assuntos
Algoritmos , Tomografia Computadorizada de Feixe Cônico/métodos , Neoplasias de Cabeça e Pescoço/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Humanos
20.
J Appl Clin Med Phys ; 19(5): 539-546, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-29992732

RESUMO

BACKGROUND AND PURPOSE: Chest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints. MATERIALS AND METHODS: Twenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees. RESULTS: Univariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis. CONCLUSIONS: Using machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis.


Assuntos
Carcinoma Pulmonar de Células não Pequenas/radioterapia , Neoplasias Pulmonares/radioterapia , Aprendizado de Máquina , Atividades Cotidianas , Humanos , Radiocirurgia , Parede Torácica
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