Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer.

Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.

The future of MRI in radiation therapy: Challenges and opportunities for the MR community.

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm.

OBJECTIVE: To distinguish benign from malignant cystic renal lesions (CRL) using a contrast-enhanced CT-based radiomics model and a clinical decision algorithm. METHODS: This dual-center retrospective study included patients over 18 years old with CRL between 2005 and 2018. The reference standard was histopathology or 4-year imaging follow-up. Training and testing datasets were acquired from two institutions. Quantitative 3D radiomics analyses were performed on nephrographic phase CT images. Ten-fold cross-validated LASSO regression was applied to the training dataset to identify the most discriminative features. A logistic regression model was trained to classify malignancy and tested on the independent dataset. Reported metrics included areas under the receiver operating characteristic curves (AUC) and balanced accuracy. Decision curve analysis for stratifying patients for surgery was performed in the testing dataset. A decision algorithm was built by combining consensus radiological readings of Bosniak categories and radiomics-based risks. RESULTS: A total of 149 CRL (139 patients; 65 years [56-72]) were included in the training dataset-35 Bosniak(B)-IIF (8.6% malignancy), 23 B-III (43.5%), and 23 B-IV (87.0%)-and 50 CRL (46 patients; 61 years [51-68]) in the testing dataset-12 B-IIF (8.3%), 10 B-III (60.0%), and 9 B-IV (100%). The machine learning model achieved high diagnostic performance in predicting malignancy in the testing dataset (AUC = 0.96; balanced accuracy = 94%). There was a net benefit across threshold probabilities in using the clinical decision algorithm over management guidelines based on Bosniak categories. CONCLUSION: CT-based radiomics modeling accurately distinguished benign from malignant CRL, outperforming the Bosniak classification. The decision algorithm best stratified lesions for surgery and active surveillance. KEY POINTS: • The radiomics model achieved excellent diagnostic performance in identifying malignant cystic renal lesions in an independent testing dataset (AUC = 0.96). • The machine learning-enhanced decision algorithm outperformed the management guidelines based on the Bosniak classification for stratifying patients to surgical ablation or active surveillance.

Intravoxel incoherent motion diffusion-weighted MRI for the characterization of inflammation in chronic liver disease.

OBJECTIVE: To evaluate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for grading hepatic inflammation. METHODS: In this retrospective cross-sectional dual-center study, 91 patients with chronic liver disease were recruited between September 2014 and September 2018. Patients underwent 3.0-T MRI examinations within 6 weeks from a liver biopsy. IVIM parameters, perfusion fraction (f), diffusion coefficient (D), and pseudo-diffusion coefficient (D*), were estimated using a voxel-wise nonlinear regression on DWI series (10 b-values from 0 to 800 s/mm2). The reference standard was histopathological analysis of hepatic inflammation grade, steatosis grade, and fibrosis stage. Intraclass correlation coefficients (ICC), univariate and multivariate correlation analyses, and areas under receiver operating characteristic curves (AUC) were assessed. RESULTS: Parameters f, D, and D* had ICCs of 0.860, 0.839, and 0.916, respectively. Correlations of f, D, and D* with inflammation grade were ρ = - 0.70, p < 0.0001; ρ = 0.10, p = 0.35; and ρ = - 0.27, p = 0.010, respectively. When adjusting for fibrosis and steatosis, the correlation between f and inflammation (p < 0.0001) remained, and that between f and fibrosis was also significant to a lesser extent (p = 0.002). AUCs of f, D, and D* for distinguishing inflammation grades 0 vs. ≥ 1 were 0.84, 0.53, and 0.70; ≤ 1 vs. ≥ 2 were 0.88, 0.57, and 0.60; and ≤ 2 vs. 3 were 0.86, 0.54, and 0.65, respectively. CONCLUSION: Perfusion fraction f strongly correlated, D very weakly correlated, and D* weakly correlated with inflammation. Among all IVIM parameters, f accurately graded inflammation and showed promise as a biomarker of hepatic inflammation. KEY POINTS: • IVIM parameters derived from DWI series with 10 b-values are reproducible for liver tissue characterization. • This retrospective two-center study showed that perfusion fraction provided good diagnostic performance for distinguishing dichotomized grades of inflammation. • Fibrosis is a significant confounder on the association between inflammation and perfusion fraction.

MRI cine-tagging of cardiac-induced motion for noninvasive staging of liver fibrosis.

BACKGROUND: MR elastography is a noninvasive technique that provides high diagnostic accuracy for the staging of liver fibrosis; however, it requires external hardware and mainly assesses the right lobe. PURPOSE: To evaluate the diagnostic performance of MRI cine-tagging for staging fibrosis in the left liver lobe, using biopsy as the reference standard. STUDY TYPE: Institutional Review Board (IRB)-approved two-center prospective study. POPULATION: Seventy-six patients with chronic liver disease who underwent an MRI cine-tagging examination and a liver biopsy within a 6-week interval. FIELD STRENGTH/SEQUENCE: 2D-GRE multislice sequence at 3.0T with spatial modulation of the magnetization preparation sequence and peripheral pulse-wave triggering on two coronal slices chosen underneath the heart apex to capture maximal deformation with consecutive breath-holds adapted to patient cardiac frequency. ASSESSMENT: A region of interest was selected in the liver close to the heart apex. Maximal strain was evaluated with the harmonic phase (HARP) technique. STATISTICAL TESTS: Spearman's correlation, Kruskal-Wallis test, Mann-Whitney U-test, and receiver operating characteristic (ROC) analysis were performed. RESULTS: Liver strain measured on tagged images decreased with higher histological fibrosis stage (ρ = -0.68, P < 0.0001). Strain values were significantly different between all fibrosis stages (P < 0.0001), and between groups of fibrosis stages ≤F3 vs. F4 (P < 0.05). Areas under the ROC curves were 0.95 (95% confidence interval: 0.89-1.00) to distinguish fibrosis stages F0 vs. F4, 0.81 (0.70-0.92) for stages F0 vs. ≥F1, 0.84 (0.76-0.93) for stages ≤F1 vs. ≥F2, 0.86 (0.78-0.94) for stages ≤F2 vs. ≥F3, and 0.87 (0.77-0.96) for stages ≤F3 vs. F4. DATA CONCLUSION: MRI cine-tagging is a promising technique for measuring liver strain without additional elastography hardware. It could be used to assess the left liver lobe as a complement to current techniques assessing the right lobe. LEVEL OF EVIDENCE: 1 Technical Efficacy: 3 J. Magn. Reson. Imaging 2020;51:1570-1580.

Prospective comparison of transient, point shear wave, and magnetic resonance elastography for staging liver fibrosis.

OBJECTIVES: To perform head-to-head comparisons of the feasibility and diagnostic performance of transient elastography (TE), point shear-wave elastography (pSWE), and magnetic resonance elastography (MRE). METHODS: This prospective, cross-sectional, dual-center imaging study included 100 patients with known or suspected chronic liver disease caused by hepatitis B or C virus, nonalcoholic fatty liver disease, or autoimmune hepatitis identified between 2014 and 2018. Liver stiffness measured with the three elastographic techniques was obtained within 6 weeks of a liver biopsy. Confounding effects of inflammation and steatosis on association between fibrosis and liver stiffness were assessed. Obuchowski scores and AUCs for staging fibrosis were evaluated and the latter were compared using the DeLong method. RESULTS: TE, pSWE, and MRE were technically feasible and reliable in 92%, 79%, and 91% subjects, respectively. At univariate analysis, liver stiffness measured by all techniques increased with fibrosis stages and inflammation and decreased with steatosis. For classification of dichotomized fibrosis stages, the AUCs were significantly higher for distinguishing stages F0 vs. ≥ F1 with MRE than with TE (0.88 vs. 0.71; p < 0.05) or pSWE (0.88 vs. 0.73; p < 0.05), and for distinguishing stages ≤ F1 vs. ≥ F2 with MRE than with TE (0.85 vs. 0.75; p < 0.05). TE, pSWE, and MRE Obuchowski scores for staging fibrosis stages were respectively 0.89 (95% CI 0.85-0.93), 0.90 (95% CI 0.85-0.94), and 0.94 (95% CI 0.91-0.96). CONCLUSION: MRE provided a higher diagnostic performance than TE and pSWE for staging early stages of liver fibrosis. TRIAL REGISTRATION: NCT02044523 KEY POINTS: • The technical failure rate was similar between MRE and US-based elastography techniques. • Liver stiffness measured by MRE and US-based elastography techniques increased with fibrosis stages and inflammation and decreased with steatosis. • MRE provided a diagnostic accuracy higher than US-based elastography techniques for staging of early stages of histology-determined liver fibrosis.

[[The Lactéol's laboratory of Dr Boucard (Laboratoire du Dr Boucard]. / Le laboratoire du Lactéol du Docteur Boucard.

Shortly before 1910, Dr Boucard creates his laboratory in Paris. It manufactures and sells a drug based on lactic ferments the " Lactéol du Dr Boucard" (Dr's Boucard Lactéol) that will make the fortune of the physician. The article explains Dr Boucard's life and his relationship with the arts (painting and photography), and tells the story of his laboratory until the 2000s, referring to the pharmacists who succeeded them, as well as the various buildings where were elaborated Lactéol's variants.

[Rise and fall of Lactarium Linas]. / Grandeur et décadence du Lactarium Linas.

The pharmacist Gabriel Linas (1863-1951) founded in Versailles (France), in 1904, a model establishment: the "milk-bank Linas". This article discusses the history of this establishment and the judicial problems of its owner during the First World War.

[Who coined the French word "coricide"?]. / Qui a inventé le mot "coricide"?

In this article, the author examines the claims of the pharmacist Adolphe René Le Brun, assignee of the trademark "Coricide russe". Litigation and trials are reported. Some hypotheses are studied. But for now, no final award is considered.

[Eugène-Humbert Guitard and French Institute of History of Science (1932-1939)]. / Eugène-Humbert Guitard et l'Institut d'histoire des sciences et des techniques (1932-1939).

Named "membre-conseil" of the French Institute of History of Science in April 1932, Guitard gives there, from January 1935 to February 1939, a dozen history of pharmacy conferences. Those conferences will give birth to her especially valuable Manuel d'histoire de la littérature pharmaceutique, published in 1942 by Caffin. The author re-examines this intellectual adventure.

[The end of an era. Fourteen radio sketches for the Pink Pills]. / La fin d'une époque. Quatorze saynètes radiophoniques pour le compte des pilules Pink.

Those sketches, restaured by the French "Institut national de l'audiovisuel", are transcribed and analyzed for the first time. They was probably broadcasted during the summer of 1939 by the private station Radio Gard Nîmes.

[A series of radio broadcasts of the French School Radio devoted to Pharmacy in 1966]. / Une série d'émissions de la Radio scolaire consacrée à la pharmacie en 1966.

In December 1966, the French School Radio devoted three of its emissions to Pharmacy. Found in the archives of the National Center for Educational Documentation (CNDP), those short programs resumed life.

[The "catgut's" laboratory of Auguste Fandre in Nancy]. / Les laboratoires de catgut d'Auguste Fandre, à Nancy.

Auguste Fandre, owner of a pharmacy in Nancy since 1906, specializes his enterprise in biological analyses and in the production of sterilized serums and blisters. In 1907, he imagines the "Catgut Fandre". During the First World War, his production becomes enormous and he must reorganize his laboratory. At the beginning of the 1920s, he undertakes the construction of moderns installations behind the pharmacy. These buildings are used up to the 1980s and then the laboratory is transfered to Ludres in the vicinity of Nancy. In the middle of these years, the old buildings are destroyed; they have been replaced by a fallow ground.

[1913: A hundred years ago, the Society of the History of Pharmacy was born]. / Il y a cent ans naissait la Société d'histoire de la pharmacie.

The "Société d'histoire de la pharmacie" was chronologically the first society for the history of pharmacy in the world. It was initiated by a triumvirate : Eugene-Humbert Guitard (1884-1976), a young graduate of the "Ecole des chartes", Charles Buchet (1848-1933), director of an important pharmaceutical cooperative named "Pharmacie central de France", and Paul Dorveaux (1851-1938), the librarian of the Superior School of Pharmacy of Paris. Those three men were perfectly complementary: the pharmacist Buchet was the "financial", the doctor Dorveaux was the "strategist" and Guitard was in charge of the practical realization. To carry out this project, they appealed to men of good will. Finally, on February 1, 1913, seventy founding members gave birth to the "Societe d'histoire de la pharmacie". These founders came from diverse backgrounds. Some of them, such Dorveaux, belonged to the "Société française d'histoire de la medecine", founded ten years ago. Others were members of the Supervisory Committee of the "Pharmacie centrale de France". A third group consisted of university teachers, as Leon Guignard, the former director of the Superior School of Pharmacy of Paris. There was also some archivists and writers, for example Camille Bloch, Ferdinand Brunot and, of course, Eugene-Humbert Guitard. The largest contingent was made up of French historians and collectors, like Antoine Balland and Charles-Henri Fialon. There was also four foreign founders: the Danish John William Schibbye Johnsson (1868-1929), the American Edward Kremers (1865-1941), and the Germans Hermann Emil Schelenz (1848-1922) and Karl Sudhoff (1853-1938). All of them were correspondents and friends of Paul Dorveaux. As Guitard wrote in 1938, Dorveaux office was, at that time, the "world center" of the studies ofhistory of pharmacy. Morever, according Guitard, the new group could have be called "Societe internationale d'histoire de la pharmacie". The term "international" seemed unfortunately too connoted at this time (synonymous ofworkerism!). So it was abandonned. The World War I has partially ended this aspiration to international cooperation. France and Germany was far apart from each other. And World War II further complicated this situation. France has organized its first international Congress of History of Pharmacy at Paris in 1973: it was already the 21st Congress organized by the International Society for the History of Pharmacy! Fortunately, two others followed: in 1995 and 2013.

[The film library of Sandoz]. / L'épopée de la cinémathèque Sandoz.

During nearly forty years, the Cinémathèque Sandoz helped the initial formation of students and the training of doctors and pharmacists. Half a century after the produc tion of one of his most memorable films, Le Horla (Jean-Daniel Pollet), the author provides a brief history of those cultural sponsorship activites. It presents the main leaders of the cinémathèque et some of its iconic projets.

Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.

Mesoscopic photoacoustic imaging (PAI) enables label-free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time-consuming and error-prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human-annotated ground-truth labels. To address this, an unsupervised image-to-image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D photoacoustic images. Applied to a diverse range of in silico, in vitro, and in vivo data, including patient-derived breast cancer xenograft models and 3D clinical angiograms, VAN-GAN demonstrates its capability to facilitate accurate and unbiased segmentation of 3D vascular networks. By leveraging synthetic data, VAN-GAN reduces the reliance on manual labeling, thus lowering the barrier to entry for high-quality blood vessel segmentation (F1 score: VAN-GAN vs. U-Net = 0.84 vs. 0.87) and enhancing preclinical and clinical research into vascular structure and function.

Performance evaluation of mesoscopic photoacoustic imaging.

Photoacoustic mesoscopy visualises vascular architecture at high-resolution up to ~3 mm depth. Despite promise in preclinical and clinical imaging studies, with applications in oncology and dermatology, the accuracy and precision of photoacoustic mesoscopy is not well established. Here, we evaluate a commercial photoacoustic mesoscopy system for imaging vascular structures. Typical artefact types are first highlighted and limitations due to non-isotropic illumination and detection are evaluated with respect to rotation, angularity, and depth of the target. Then, using tailored phantoms and mouse models, we investigate system precision, showing coefficients of variation (COV) between repeated scans [short term (1 h): COV= 1.2%; long term (25 days): COV= 9.6%], from target repositioning (without: COV=1.2%, with: COV=4.1%), or from varying in vivo user experience (experienced: COV=15.9%, unexperienced: COV=20.2%). Our findings show robustness of the technique, but also underscore general challenges of limited-view photoacoustic systems in accurately imaging vessel-like structures, thereby guiding users when interpreting biologically-relevant information.

Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI.

PURPOSE: Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI). PATIENTS AND METHODS: This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances. RESULTS: With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology. CONCLUSION: The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.

[Radium in pharmacies! First Part : Pharmaceutical used of radium before the first World War]. / Du radium dans les pharmacies! 1 Première partie: Les usages pharmaceutiques du radium avant la première Guerre mondiale.

The authors present in this part the creation of first radioactiv drugs, since the discovery of radium to the first World War. They present the industrial and chemist Emile Armet de Lisle and the pharmacist Alexandre Antonin Jaboin, first manufacturers of those new way of treatment.