Evaluation of importance estimators in deep learning classifiers for Computed Tomography

Deep learning has shown superb performance in detecting objects and classifying images, ensuring a great promise for analyzing medical imaging. Translating the success of deep learning to medical imaging, in which doctors need to understand the underlying process, requires the capability to interpret and explain the prediction of neural networks. Interpretability of deep neural networks often relies on estimating the importance of input features (e.g., pixels) with respect to the outcome (e.g., class probability). However, a number of importance estimators (also known as saliency maps) have been developed and it is unclear which ones are more relevant for medical imaging applications. In the present work, we investigated the performance of several importance estimators in explaining the classification of computed tomography (CT) images by a convolutional deep network, using three distinct evaluation metrics. First, the model-centric fidelity measures a decrease in the model accuracy when certain inputs are perturbed. Second, concordance between importance scores and the expert-defined segmentation masks is measured on a pixel level by a receiver operating characteristic (ROC) curves. Third, we measure a region-wise overlap between a XRAI-based map and the segmentation mask by Dice Similarity Coefficients (DSC). Overall, two versions of SmoothGrad topped the fidelity and ROC rankings, whereas both Integrated Gradients and SmoothGrad excelled in DSC evaluation. Interestingly, there was a critical discrepancy between model-centric (fidelity) and human-centric (ROC and DSC) evaluation. Expert expectation and intuition embedded in segmentation maps does not necessarily align with how the model arrived at its prediction. Understanding this difference in interpretability would help harnessing the power of deep learning in medicine.Comment: 4th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems (EXTRAAMAS 2022) - International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS

Caractérisation multiparamétrique des cancers colorectaux

Imaging is the principal tool for diagnosis, extension assessment and therapeutic follow-up of the vast majority of tumors. Computed tomography (CT) is the most used method and provides an assessment of tumor tissue density. In-depth analysis of those images acquired in clinical routine has suppl ied additional data regarding patient survival or treatment response. All those new data allow to describe the tumor phenotype and are generally grouped under the generic term radiomics. Most of previous studies focused on texture analysis using contrast enhanced CT (portal phase). In the first part of this thesis, we carried out a radiomics analysis of both contrast-enhanced and nonenhanced CT images of the colorectal tumors.Imaging is the principal tool for diagnosis, extension assessment and therapeutic followup of the vast majority of tumors. Computed tomography (CT) is the most used method and provides an assessment of tumor tissue density. In-depth analysis of those images acquired in clinical routine has supplied additional data regarding patient survival or treatment response. All those new data allow to describe the tumor phenotype and are generally grouped under the generic term radiomics. Most of previous studies focused on texture analysis using contrast enhanced CT (portal phase). In the first part of this thesis, we carried out a radiomics analysis of both contrast-enhanced and non-enhanced CT images of the colorectal tumors.L’imagerie est un outil pour réaliser le diagnostic, le bilan d’extension et le suivi thérapeutique de la grande majorité des tumeurs. La tomodensitométrie (TDM) est la méthode la plus utilisée et les images obtenues fournissent une cartographie tumorale fondée sur la densité des tissus. L’analyse plus approfondie de ces images acquises en routine clinique a permis d’extraire des informations supplémentaires quant à la survie du patient ou à la réponse au(x) traitement(s). Toutes ces nouvelles données permettent de décrire le phénotype d’une lésion de façon non invasive et sont regroupées sous le terme de radiomique. La plupart des études de radiomique se sont focalisées sur les paramètres de texture et ont évalué les données acquises à l’aide de TDM avec injection de produit de contraste (phase portale). Pour ces travaux de thèse, nous avons réalisé une analyse des paramètres de radiomique extraits à la fois des images TDM contrastées et non contrastées des tumeurs colorectales. La construction d’un modèle pronostique à l’aide de ces paramètres a permis d’étudier la complémentarité des informations fournies par les deux modalités. Dans un second temps, l’analyse des modifications transcriptomiques des cellules souches et cellules cancéreuses dans le cancer colorectal a permis de valider l’hypothèse que la quantification de modifications transcriptomiques peut également avoir une valeur pronostique. Finalement, l’étude des corrélations entre les données d’expression génétique et la radiomique en TDM a montré que la quantification de l’hétérogénéité tumorale en TDM reflète en partie les modifications transcriptomiques

Multiparametric characterization of colorectal cancer

L’imagerie est un outil pour réaliser le diagnostic, le bilan d’extension et le suivi thérapeutique de la grande majorité des tumeurs. La tomodensitométrie (TDM) est la méthode la plus utilisée et les images obtenues fournissent une cartographie tumorale fondée sur la densité des tissus. L’analyse plus approfondie de ces images acquises en routine clinique a permis d’extraire des informations supplémentaires quant à la survie du patient ou à la réponse au(x) traitement(s). Toutes ces nouvelles données permettent de décrire le phénotype d’une lésion de façon non invasive et sont regroupées sous le terme de radiomique. La plupart des études de radiomique se sont focalisées sur les paramètres de texture et ont évalué les données acquises à l’aide de TDM avec injection de produit de contraste (phase portale). Pour ces travaux de thèse, nous avons réalisé une analyse des paramètres de radiomique extraits à la fois des images TDM contrastées et non contrastées des tumeurs colorectales. La construction d’un modèle pronostique à l’aide de ces paramètres a permis d’étudier la complémentarité des informations fournies par les deux modalités. Dans un second temps, l’analyse des modifications transcriptomiques des cellules souches et cellules cancéreuses dans le cancer colorectal a permis de valider l’hypothèse que la quantification de modifications transcriptomiques peut également avoir une valeur pronostique. Finalement, l’étude des corrélations entre les données d’expression génétique et la radiomique en TDM a montré que la quantification de l’hétérogénéité tumorale en TDM reflète en partie les modifications transcriptomiques.Imaging is the principal tool for diagnosis, extension assessment and therapeutic follow-up of the vast majority of tumors. Computed tomography (CT) is the most used method and provides an assessment of tumor tissue density. In-depth analysis of those images acquired in clinical routine has suppl ied additional data regarding patient survival or treatment response. All those new data allow to describe the tumor phenotype and are generally grouped under the generic term radiomics. Most of previous studies focused on texture analysis using contrast enhanced CT (portal phase). In the first part of this thesis, we carried out a radiomics analysis of both contrast-enhanced and nonenhanced CT images of the colorectal tumors.Imaging is the principal tool for diagnosis, extension assessment and therapeutic followup of the vast majority of tumors. Computed tomography (CT) is the most used method and provides an assessment of tumor tissue density. In-depth analysis of those images acquired in clinical routine has supplied additional data regarding patient survival or treatment response. All those new data allow to describe the tumor phenotype and are generally grouped under the generic term radiomics. Most of previous studies focused on texture analysis using contrast enhanced CT (portal phase). In the first part of this thesis, we carried out a radiomics analysis of both contrast-enhanced and non-enhanced CT images of the colorectal tumors

Conversion of Vertical Banded Gastroplasty to Laparoscopic Gastric Bypass: a Step-By-Step Teaching Video

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Colorectal Cancer Radiogenomics

International audienc

Radiogenomics in Colorectal Cancer

The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments

Colorectal Cancer Radiogenomics

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Primary Closure Versus Biliary Drainage After Laparoscopic Choledocotomy: Results of a Comparative Study.

International audienceTo evaluate the feasibility, safety, and short-term outcomes of primary closure (PC) and biliary drainage (BD), after the laparoscopic treatment of common bile duct (CBD) stones by choledocotomy.Between January 2009 and December 2014, 102 patients underwent laparoscopy for lithiasis of the CBD. Intraoperative cholangiography was systematically performed, followed by choledocoscopy, depending on the size of the CBD.Eighty (78.4%) of the 102 patients underwent laparoscopic stone extraction by choledocotomy, and were assigned to 2 groups: PC (group A, n=25), and BD (group B, n=55). Groups A and B were comparable in terms of age (62.3±26.1 vs. 66.0±19.3 y; P=0.53), the percentage of women (72.0% vs. 76.4%; P=0.68), body mass index (25.9±6.1 vs. 26.9±4.4 kg/m; P=0.52), and CBD diameter (11.6±3.1 vs. 12.1±3.8 mm; P=0.59). The mean durations of surgery and of hospital stay were significantly shorter in group A: 179±38 versus 211±57 minutes (P=0.02) and 5.4±2.0 versus 8.4±3.2 days (P<0.001). Groups A and B were comparable in terms of serious postoperative morbidity (Clavien-Dindo scores of 3, 4, and 5): 2 versus 4 (P=1). In group B, bile drain removal was complicated by choleperitoneum in 3 cases.With shorter durations of surgery and hospital stay, equivalent postoperative morbi-mortality, and an absence of the specific morbidity due to bile drainage, PC may be considered a safe and feasible option for the laparoscopic management of CBD stones by choledocotomy

Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer