[Contribution of connected prescriptions (NetSIG) for the management of molecular pathology exams]. / Apport de la prescription connectée (NetSIG) dans la prise en charge et la gestion des examens de pathologie moléculaire.

INTRODUCTION: This study describes our experience implementing a connected prescription software (NetSIG, Terascop) for molecular pathology exams. MATERIAL AND METHODS: NetSIG was set up for liquid biopsies and tissue testing. After registration and activation of regional pathology laboratories, NetSIG was implemented for external then internal prescriptions. RESULTS: NetSIG allows users to follow up on all prescriptions on the website, to interact through messages and to consult reports after validation. External set up was quick (3-4 months) and comprehensive (>70%). Prescriptions were made by physicians or more often by secretaries or referring pathologists. Internal prescriptions were made by pathologists then registered in NetSIG by our secretaries. This deployment strategy has resulted in very good completeness of prescriptions (>90%). DISCUSSION AND CONCLUSION: Connected prescriptions made this complex circuit more fluid and facilitated the redistribution of different administrative and technical tasks. The number of phone calls decreased sharply. Half of the prescriptions were made by pathologists and half by oncologists (physicians or secretaries). The mean dearchiving duration for blocks was one day. Mean forwarding of blocks was 2.5 days. Mean turnaround time was 8 days for targeted techniques and 13 days for Next Generation Sequencing. Physicians appreciated the interactivity of the software and the fact that they could consult it on a smartphone.

Deep learning automation of MEST-C classification in IgA nephropathy.

BACKGROUND: Although the MEST-C classification is among the best prognostic tools in immunoglobulin A nephropathy (IgAN), it has a wide interobserver variability between specialized pathologists and others. Therefore we trained and evaluated a tool using a neural network to automate the MEST-C grading. METHODS: Biopsies of patients with IgAN were divided into three independent groups: the Training cohort (n = 42) to train the network, the Test cohort (n = 66) to compare its pixel segmentation to that made by pathologists and the Application cohort (n = 88) to compare the MEST-C scores computed by the network or by pathologists. RESULTS: In the Test cohort, >73% of pixels were correctly identified by the network as M, E, S or C. In the Application cohort, the neural network area under the receiver operating characteristics curves were 0.88, 0.91, 0.88, 0.94, 0.96, 0.96 and 0.92 to predict M1, E1, S1, T1, T2, C1 and C2, respectively. The kappa coefficients between pathologists and the network assessments were substantial for E, S, T and C scores (kappa scores of 0.68, 0.79, 0.73 and 0.70, respectively) and moderate for M score (kappa score of 0.52). Network S and T scores were associated with the occurrence of the composite survival endpoint (death, dialysis, transplantation or doubling of serum creatinine) [hazard ratios 9.67 (P = .006) and 7.67 (P < .001), respectively]. CONCLUSIONS: This work highlights the possibility of automated recognition and quantification of each element of the MEST-C classification using deep learning methods.

Automated evaluation with deep learning of total interstitial inflammation and peritubular capillaritis on kidney biopsies.

BACKGROUND: Interstitial inflammation and peritubular capillaritis are observed in many diseases on native and transplant kidney biopsies. A precise and automated evaluation of these histological criteria could help stratify patients' kidney prognoses and facilitate therapeutic management. METHODS: We used a convolutional neural network to evaluate those criteria on kidney biopsies. A total of 423 kidney samples from various diseases were included; 83 kidney samples were used for the neural network training, 106 for comparing manual annotations on limited areas to automated predictions, and 234 to compare automated and visual gradings. RESULTS: The precision, recall and F-score for leukocyte detection were, respectively, 81%, 71% and 76%. Regarding peritubular capillaries detection the precision, recall and F-score were, respectively, 82%, 83% and 82%. There was a strong correlation between the predicted and observed grading of total inflammation, as for the grading of capillaritis (r = 0.89 and r = 0.82, respectively, all P < .0001). The areas under the receiver operating characteristics curves for the prediction of pathologists' Banff total inflammation (ti) and peritubular capillaritis (ptc) scores were respectively all above 0.94 and 0.86. The kappa coefficients between the visual and the neural networks' scores were respectively 0.74, 0.78 and 0.68 for ti ≥1, ti ≥2 and ti ≥3, and 0.62, 0.64 and 0.79 for ptc ≥1, ptc ≥2 and ptc ≥3. In a subgroup of patients with immunoglobulin A nephropathy, the inflammation severity was highly correlated to kidney function at biopsy on univariate and multivariate analyses. CONCLUSION: We developed a tool using deep learning that scores the total inflammation and capillaritis, demonstrating the potential of artificial intelligence in kidney pathology.

[Contribution and limits of lean management in the organization and working of a pathology department]. / Apports et limites du lean management dans l'organisation et le fonctionnement d'un service de Pathologie.

INTRODUCTION: In order to validate our strategy of continuous improvement and to identify new ways to increase performance, an evaluation of all the procedures was conducted in our department using the principles of lean management. MATERIAL AND METHODS: Lean-6-sigma methodology (Gemba Walk, Value StreamMapping, spaghetti diagram, Kaizen workshop and priorization matrix) was used to analyze the procedures of the conventional and molecular sectors, and to identify bottlenecks, actions without added value and solutions. RESULTS: The audit identified bottlenecks in pre-analytical (registration), analytical (cytology, immunohistochemistry, sequencing, pathologists) and post-analytical processes (absence of secretaries, delivery of reports by mail). It underlined a suboptimal flow of people and materials, the heavy impact of an increasing work load (8%/year) in reception and microscopy even though we had outsourced, and an often critical work place schedule for technicians which prevent them from achieving tasks without added value (quality control, validation of methods and protocols) or even daily tasks (cutting, immunohistochemistry). After completing the 72 actions aimed at managing overproduction, improving working conditions and developing new activities, turn-around time was partially under control and the automation process was well advanced. DISCUSSION AND CONCLUSION: The audit validated our strategy of continuous improvement and advanced the standardization of our working conditions. Even if the turn-around time for reports was shortened, the audit initiated a positive medical and technical dynamic that should help us to implement the next steps of our reorganization (automation and extension of the department).

Recognition of intraglomerular histological features with deep learning in protocol transplant biopsies and their association with kidney function and prognosis.

Background: The Banff Classification may not adequately address protocol transplant biopsies categorized as normal in patients experiencing unexplained graft function deterioration. This study seeks to employ convolutional neural networks to automate the segmentation of glomerular cells and capillaries and assess their correlation with transplant function. Methods: A total of 215 patients were categorized into three groups. In the Training cohort, glomerular cells and capillaries from 37 patients were manually annotated to train the networks. The Test cohort (24 patients) compared manual annotations vs automated predictions, while the Application cohort (154 protocol transplant biopsies) examined predicted factors in relation to kidney function and prognosis. Results: In the Test cohort, the networks recognized histological structures with Precision, Recall, F-score and Intersection Over Union exceeding 0.92, 0.85, 0.89 and 0.74, respectively. Univariate analysis revealed associations between the estimated glomerular filtration rate (eGFR) at biopsy and relative endothelial area (r = 0.19, P = .027), endothelial cell density (r = 0.20, P = .017), mean parietal epithelial cell area (r = -0.38, P < .001), parietal epithelial cell density (r = 0.29, P < .001) and mesangial cell density (r = 0.22, P = .010). Multivariate analysis retained only endothelial cell density as associated with eGFR (Beta = 0.13, P = .040). Endothelial cell density (r = -0.22, P = .010) and mean podocyte area (r = 0.21, P = .016) were linked to proteinuria at biopsy. Over 44 ± 29 months, 25 patients (16%) reached the primary composite endpoint (dialysis initiation, or 30% eGFR sustained decline), with relative endothelial area, mean endothelial cell area and parietal epithelial cell density below medians linked to this endpoint [hazard ratios, respectively, of 2.63 (P = .048), 2.60 (P = .039) and 3.23 (P = .019)]. Conclusion: This study automated the measurement of intraglomerular cells and capillaries. Our results suggest that the precise segmentation of endothelial and epithelial cells may serve as a potential future marker for the risk of graft loss.

Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples.

BACKGROUND AND OBJECTIVES: The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a deep-learning approach could save time and provide more precise data. This work aimed to develop a free tool to automatically obtain kidney histologic prognostic features. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In total, 241 samples of healthy kidney tissue were split into three independent cohorts. The "Training" cohort (n=65) was used to train two convolutional neural networks: one to detect the cortex and a second to segment the kidney structures. The "Test" cohort (n=50) assessed their performance by comparing manually outlined regions of interest to predicted ones. The "Application" cohort (n=126) compared prognostic histologic data obtained manually or through the algorithm on the basis of the combination of the two convolutional neural networks. RESULTS: In the Test cohort, the networks isolated the cortex and segmented the elements of interest with good performances (>90% of the cortex, healthy tubules, glomeruli, and even globally sclerotic glomeruli were detected). In the Application cohort, the expected and predicted prognostic data were significantly correlated. The correlation coefficients r were 0.85 for glomerular volume, 0.51 for glomerular density, 0.75 for interstitial fibrosis, 0.71 for tubular atrophy, and 0.73 for vascular intimal thickness, respectively. The algorithm had a good ability to predict significant (>25%) tubular atrophy and interstitial fibrosis level (receiver operator characteristic curve with an area under the curve, 0.92 and 0.91, respectively) or a significant vascular luminal stenosis (>50%) (area under the curve, 0.85). CONCLUSION: This freely available tool enables the automated segmentation of kidney tissue to obtain prognostic histologic data in a fast, objective, reliable, and reproducible way.

Distribution of donor-specific antibodies in the cortex and the medulla of renal transplants with chronic allograft nephropathy.

BACKGROUND: Emerging data suggest that donor-specific HLA antibodies should be more frequently found onto the transplant itself than in the bloodstream. It is now possible to detect such antibodies in kidney transplant needle biopsy samples by flow cytometry. In order to know if the detection of antibodies into such blind biopsy samples depends of the site of the biopsy, we have studied the distribution of antibodies in both the cortex and medulla of 12 transplants removed after graft loss due to chronic allograft nephropathy, and in 10 controls. METHODS: Donor-specific HLA antibodies were extracted from the cortex and the medulla of each removed transplant by an acid elution and characterized by Luminex assays. RESULTS: They were found in 58.3% of transplants with chronic allograft nephropathy and never in other kidney samples. The same antibodies were found in the bloodstream at the time of transplantectomy in only 16.6% of the recipients. The distribution between the cortex and medulla was concordant in 75% of cases. However, we observed 2 discrepancies: one in favor of the cortex and one in favor of the medulla. A majority (5/7) transplants with CAN and intra-graft donor-specific antibodies had also C4d deposits along peritubular capillaries. CONCLUSION: Testing for donor-specific HLA antibodies in kidney transplant needle biopsies can be of value provided the biopsy includes both the cortex and the medulla.

[Gastric metastasis from ovarian carcinoma revealed by a gastro-splenic perforation]. / Métastase gastrique d'un carcinome ovarien révélée par une perforation gastro-splénique.

Metastatic disease involving the stomach is unusual. We report the case of a gastric metastasis from ovarian cancer revealed by gastro-splenic perforation. The gastric metastasis was diagnosed 17 years after the diagnosis of primary cancer.

Tumor volume and metabolism of prostate cancer determined by proton magnetic resonance spectroscopic imaging at 3T without endorectal coil reveal potential clinical implications in the context of radiation oncology.

PURPOSE: To determine whether a relationship exists between the tumor volume (TV) or relative choline content determined using magnetic resonance spectroscopy imaging (MRSI) at 3T and the clinical prognostic parameters for patients with localized prostate cancer (PCa). METHODS AND MATERIALS: A total of 72 men (mean age, 67.8 ± 6.2 years) were stratified as having low-risk (n = 26), intermediate-risk (n = 24), or high-risk (n = 22) PCa. MRSI was performed at 3T using a phased-array coil. Spectra are expressed as the total choline/citrate, total choline plus creatine/citrate, and total choline plus polyamines plus creatine/citrate ratios. The mean ratio of the most pathologic voxels and the MRSI-based TV were also determined. RESULTS: The mean values of the total choline/citrate, total choline plus creatine/citrate, and total choline plus polyamine plus creatine/citrate ratios were greater for Stage T2b or greater tumors vs. Stage T2a or less tumors: 7.53 ± 13.60 vs. 2.31 ± 5.65 (p = .018), 8.98 ± 14.58 vs. 2.56 ± 5.70 (p = .016), and 10.32 ± 15.47 vs. 3.55 ± 6.16 (p = .014), respectively. The mean MRSI-based TV for Stage T2b or greater and Stage T2a or less tumors was significantly different (2.23 ± 2.62 cm(3) vs. 1.26 ± 2.06 cm(3), respectively; p = .030). This TV correlated with increased prostate-specific antigen levels (odds ratio, 1.293; p = .012). Patients with high-risk PCa had a larger TV than did the patients with intermediate-risk PCa. A similar result was found for the intermediate-risk group compared with the low-risk group (odds ratio, 1.225; p = .041). CONCLUSION: Biomarkers expressing the relative choline content and TV were significant parameters for the localization of PCa and could be helpful for determining the prognosis more accurately.

Patent ductus arteriosus ligation: the LigaSure system may be unreliable.

Surgical ligation of a patent ductus arteriosus (PDA) in small premature infants may be performed with open thoracotomy or video-assisted thoracoscopic surgery (VATS). The LigaSure vessel sealing system (Valleylab/Tyco Healthcare, Boulder, CO) is increasingly being used because of its effectiveness in promoting coagulation. Moreover, for PDA ligation using VATS, the LigaSure system seems more practical than vascular clips. Among 81 children, including 43 premature infants weighing less than 1000 grams operated on for PDA in our institution, one 9-month-old boy weighing 7600 grams underwent PDA ligation using a LigaSure grasp. The perioperative aspect of the closed ductus was satisfactory. The following day, however, ultrasound control revealed recanalization of the ductus, and the child had to undergo a second operation. At operation, the ductus wall adventia and media appeared to have retracted to both extremities, leaving the intima exposed and pulsating under the blood pressure. The PDA ligation was repeated, but in conditions of severe hemorrhage. The LigaSure system works by fusing collagen in the tissue. However, because the ductus wall has less collagen than any other vessels in the body, the LigaSure vessel sealing system is not reliable for PDA ligation.