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Vieillard Fl. I—II, Tromb.

French-English Dictionary (35,273 Entries)

I—II, Basse comp. Thomas Gounet see H 39—47 below; comp. Salle Herz, 3 February pub. Stuttgart, 29 December pub.


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April ; contains melodies from H 6, H 29 Herminie Un bal rev. Conservatoire, Dimanche, 9 December , cond. Habeneck further minor revisions —45; piano reduction by Liszt pub. August ; pub. Conservatoire, 22 December pub. May — July 1st perf. Conservatoire, 14 April pub. June 1st perf. Conservatoire, 9 December , cond. Habeneck vocal score i, iii, iv pub.


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October ; pub. Conservatoire, 30 December pub. London, 29 June ; Pauline Viardot , contralto pub. Huber arr. Conservatoire, 22 November pub. Conservatoire, 23 November piano reduction by Liszt pub. Conservatoire, 22 November vocal score pub. I—IV, Hb. I—II, Cor ang. I—IV, Tromp. I—XII, Tromb. Invalides, 5 December , cond.

Honoré de Balzac

Habeneck pub. Air Teresa : 23 bars recit. Les belles fleurs! A nous voisines et servantes! Air Fieramosca : bars Ah! I—IV, C. Bruits lontains de concert et de bal. TB—TB semi-ch. I—II, C. Berlioz pub. Gotha, 6 February pub. Leipzig, 23 February pub. Vienna, 29 November full score pub. Cirque Olympique, 6 April pub. Budapest, 15 February incorporated with a new coda in H La damnation de Faust ; vocal score pub. Lille, 14 June vocal score pub. St-Eustache, 30 April rev. Petit oiseau: chanson de paysan , T or Mez or Bar, Pf.

Salle Herz, 10 December pub. I—II, Cor. Baden-Baden, 29 August pub. Vries [Docteur Noir? Baden-Baden, 27 August pub. Baden-Baden, 9 August vocal score pub. Category : Composer Composition Lists. Lost Fl. Romance anonymous Chant, Pf. Arrangements: music by various composers Chant, Guitar arr. Pleure, pauvre Colette. March December Romance Du Boys Chant, Pf. Lost Oratorio with Latin Vulgate text comp.

Les Francs-juges. Opera in three acts Ferrand ; overture and 5 numbers surviving comp. Nise, Sopr. May Incomplete intermezzo in one act based on Les francs-juges comp. November — January Grande Ouverture des Francs-Juges. Grande Ouverture de Waverley. Cantate Pierre-Ange Vieillard , S, orch comp. Chanson gothique Goethe trans.

Johann Wolfgang von Goethe trans. Lost Les Orientales , Victor Hugo comp. Le ballet des ombres. Irish Melodies, Thomas Moore, trans. Ballade, arr.

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Ballade, Chant, Pf. La belle voyageuse. Lost arr. Ballade, S or T, Pf. Hymne des Marseillais. Arrangement of music by Rouget de Lisle arr. Kern Holoman. Grande ouverture du Roi Lear. Intrata di Rob-Roy MacGregor. Lost Thomas Moore trans. To limit this, we selected features from 4 of the 5 partitioned datasets, leaving the validation set from the first validation. Since the accuracy and AUC of the model is consistent across all five cross-validations, we do not believe leakage had a significant effect on the model.

Physicians cannot intuitively understand the results given by the model we created: Deep Learning essentially remains a black box. With the use of a large number of variables and second order statistical data, such as radiomics, we believe this cannot be avoided. Visualization techniques such as a radiomics heatmap can only give a high-level representation of the data. These publications are hypothesis-generating studies and can help in identifying relevant prognostic or predictive factors, but their level of evidence is still low, no matter how innovative their approach is.

It is considered that a human brain can only integrate up to 5 variables in order to make an adequate decision 28 , Since oncology is relying on an increasing amount of data of different types, using computers as Clinical Decision Support Systems CDSS could become mandatory. Deep Learning, and Artificial Intelligence in a broader sense, will eventually disrupt the way we practice medicine in positive 11 , 30 , 31 and negative 32 ways. Among the disciplines poised to be radically changed, is medical imaging. Several studies have recently been published predicting longevity from routine CT-Scans 18 or detecting pneumonia from chest X-Ray The development of Deep Learning will transform the way we use imaging for diagnosis, treatment planning and decision making.

It is not clear yet if these methods should be assessed as any other medical device in a randomized trial or if new approaches are needed. In this proof-of-concept study, we show that using a DNNClassifier on heterogeneous data combining clinical and radiomics features is feasible and can accurately predict patients who will have a complete pathological response after neo-adjuvant chemoradiotherapy for locally-advanced rectal cancer.

For this subset of patients, conservative treatments could be a valid approach, with less long-term side effects. After careful prospective evaluation of this approach in a randomized clinical trial, this kind of methods could be directly implemented within the treatment planning systems used in radiation oncology to better personalize treatments.

All experiments were carried out in accordance with relevant guidelines and regulations. The study used only pre-existing medical data, therefore patient consent was not required by the ethics committee. Patients with a T N rectal adenocarcinoma treated between June and October with neo-adjuvant chemoradiation 4 to Chemoradiation was performed in our department and surgery was performed later 6 to 10 weeks by the surgery department in each recruiting institution.

Median follow-up was 16 months range: 3— Median age was 66 years old 32— Median baseline hemoglobin, neutrophils and lymphocytes counts were Delivered median doses were Median treatment length was 39 days 32— Forty-two patients received adjuvant chemotherapy. Two patients had a local relapse 2. Disease-free and overall survival rates at follow-up were All features were extracted from our clinical data warehouse CDW , that relies on the Informatics for Integrating Biology and the Bedside i2b2 model - an open source infrastructure developed by Harvard Medical School and adopted by more than academic hospitals around the world 34 , Concepts are stored separately in a hierarchical data model.

The following features were extracted: age, sex, smoking status, tumor differentiation and size, T and N stages, baseline hemoglobin, neutrophils and lymphocytes counts. Structures labels were sorted and filtered by number of occurrences. Each patient was segmented twice. In all, features were extracted features extracted for each segmentation on each 95 patients. To estimate the robustness of the tumor features, the intra-class correlation coefficient ICC was calculated 40 , ICC can be used when quantitative measurements are made on units that are organized into groups It ranges between 0 and 1, indicating null and perfect reproducibility.

R version 3. As Parmar et al. A heatmap showing radiomics features clustering and correlations to pCR was generated. Survival rates were calculated from the date of surgery to create a Kaplan-Meier curve for overall survival. All statistical analysis were performed with R version 3.

A 5-fold cross validation was performed: the original dataset was randomly partitioned into 5 equal sized subsamples. Of the 5 subsamples, a single subsample was retained as the validation data for testing the model, and the remaining 4 subsamples were used as training data. The cross-validation process was then repeated 5 times, with each of the 5 subsamples used once as the validation data. Values are reported as a mean of the 5 models. To limit test-set leakage, we calculated the ICC and the Wilcoxon correlation in 4 of the 5 partitioned datasets that were created for the 5-fold cross-validation , leaving out the validation set from the first validation.

We explored a range of combinations of batch size, layer depth and layer size. We determined the optimal architecture for this deep learning model empirically, testing numerous variants. Changing the depth of the network reduced performance. We did not increase our model depth beyond 10, hidden units due to computational constraints. The resulting DNN was a compromise between performance and computational cost and included three hidden layers with 10, 20 and 10 neurons respectively. Adagrad adapts the learning rate to the parameters, performing larger updates for infrequent and smaller updates for frequent parameters.

For this reason, it is well-suited for sparse data. The output of the network was binary pCR or no pCR. To avoid overfitting, a low number of epoch was chosen steps, 1 epoch. Training and validation was performed on a Linux Ubuntu A logistic regression model was built from the same training and testing datasets using only the TNM stage as a baseline comparison, with the glmnet R package The global analysis pipeline is shown in Fig. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The error has been fixed in the paper. National Comprehensive Cancer Network. Accessed: 27th November Maas, M. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. Habr-Gama, A. Operative versus nonoperative treatment for stage 0 distal rectal cancer following chemoradiation therapy: long-term results.

Bhangu, A. Survival outcome of local excision versus radical resection of colon or rectal carcinoma: a Surveillance, Epidemiology, and End Results SEER population-based study. Local recurrence after complete clinical response and watch and wait in rectal cancer after neoadjuvant chemoradiation: impact of salvage therapy on local disease control.

Appelt, A. High-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study. Renehan, A. Watch-and-wait approach versus surgical resection after chemoradiotherapy for patients with rectal cancer the OnCoRe project : a propensity-score matched cohort analysis. Martens, M. Cancer Inst. Aerts, H. JAMA Oncol. Nie, K. Cancer Res. Kourou, K. Machine learning applications in cancer prognosis and prediction. Kang, J. Bibault, J. Big Data and machine learning in radiation oncology: State of the art and future prospects.

Cancer Lett.

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Esteva, A. Dermatologist-level classification of skin cancer with deep neural networks. Nature , — Gulshan, V. JAMA , — Miotto, R. Oakden-Rayner, L. Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. AMIA Annu. AMIA Symp. Gillies, R. Radiology , — Lambin, P. Radiomics: the bridge between medical imaging and personalized medicine. Gan, J. Zhang, L. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Deep Radiomics Rectal Cancer: Code , parameters and scripts used for our study predicting pathologic complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer Liu, Z.

CCR Lovinfosse, P. Kumar, V. Radiomics: the process and the challenges. Imaging 30 , — Abernethy, A. Rapid-learning system for cancer care. Obermeyer, Z. Beam, A. Cabitza, F. Unintended Consequences of Machine Learning in Medicine. Rajpurkar, P. Zapletal, E. Methodology of integration of a clinical data warehouse with a clinical information system: the HEGP case. Health Technol. Rance, B. Varian Incorporated. Varian Developers Forum.