Three artificial intelligence data challenges based on CT and MRI

Lassau, Nathalie; Bousaid, Imad; Chouzenoux, Émilie; Lamarque, Jean-Philippe; Charmettant, Benoit; Azoulay, Mikael; Cotton, François; Khalil, Antoine; Lucidarme, O.; Pigneur, F.; Benaceur, Y.; Sadate, Alexandre; Léderlin, Mathieu; Laurent, François; Chassagnon, Guillaume; Ernst, Olivier; Ferreti, Gilbert; Diascorn, Yann; Brillet, Pierre Yves; Crézé, Maud; Cassagnes, L.; Caramella, Caroline; Loubet, Antoine; Dallongeville, Axel; Abassebay, N.; Ohana, Mickaël; Banaste, Nathan; Cadi, M.; Behr, Julien; Boussel, Loîc; Fournier, Laure; Zins, Marc; Beregi, Jean‐Paul; Luciani, Alain; Cotten, Anne; Méder, Jean-François

doi:10.1016/j.diii.2020.03.006

Cited by 22 publications

(3 citation statements)

References 33 publications

(26 reference statements)

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“…These include the retrospective nature and single-center design of the study, as well as the subjective definition of the vascular sign. Future research efforts should aim to validate our findings in multi-center studies and explore the potential of artificial intelligence in overcoming limitations related to imaging resolution and observer subjectivity [ 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

Ruan,

Ma,

et al. 2024

J Transl Med

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Background Neoadjuvant immunochemotherapy (NICT) plus esophagectomy has emerged as a promising treatment option for locally advanced esophageal squamous cell carcinoma (LA-ESCC). Pathologic complete response (pCR) is a key indicator associated with great efficacy and overall survival (OS). However, there are insufficient indicators for the reliable assessment of pCR. Methods 192 patients with LA-ESCC treated with NICT from December 2019 to October 2023 were recruited. According to pCR status, patients were categorized into pCR group (22.92%) and non-pCR group (77.08%). Radiological features of pretreatment and preoperative CT images were extracted. Logistic and COX regressions were trained to predict pathological response and prognosis, respectively. Results Four of the selected radiological features were combined to construct an ESCC preoperative imaging score (ECPI-Score). Logistic models revealed independent associations of ECPI-Score and vascular sign with pCR, with AUC of 0.918 in the training set and 0.862 in the validation set, respectively. After grouping by ECPI-Score, a higher proportion of pCR was observed among the high-ECPI group and negative vascular sign. Kaplan Meier analysis demonstrated that recurrence-free survival (RFS) with negative vascular sign was significantly better than those with positive (P = 0.038), but not for OS (P = 0.310). Conclusions This study demonstrates dynamic radiological features are independent predictors of pCR for LA-ESCC treated with NICT. It will guide clinicians to make accurate treatment plans.

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Section: Discussionmentioning

confidence: 99%

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

Ruan,

Ma,

et al. 2024

J Transl Med

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“…Other challenges for lung nodule detection have been organized, for example: the data challenge held at the annual congress of the French Society of Radiology in 2019 (Journées Francophones de Radiologie 2019). This challenge included 1237 chest CT examinations and participants were asked to develop AI models that detected lung nodules; calculating their volume after segmentation and classifying them as either: probably benign (volume < 100 mm 3 ) or probably malignant (volume ≥ 100 mm 3 ) nodules [29,30].…”

Section: Lung Nodule Detectionmentioning

confidence: 99%

Artificial intelligence in lung cancer: current applications and perspectives

et al. 2022

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Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deeplearning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

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“…Data challenges on medical and clinical images have already been organized in recent years. We can mention those of French radiologists since 2018, 9 , 10 and the Camelyon 16 and 17 in pathology. 11 A recent review paper reported several data challenges in pathology and what these competitions brought to this speciality.…”

Section: Introductionmentioning

confidence: 99%