The Oxford Classification of IgA nephropathy (IgAN) includes the following four histologic components: mesangial (M) and endocapillary (E) hypercellularity, segmental sclerosis (S) and interstitial fibrosis/tubular atrophy (T). These combine to form the MEST score and are independently associated with renal outcome. Current prediction and risk stratification in IgAN requires clinical data over 2 years of follow-up. Using modern prediction tools, we examined whether combining MEST with cross-sectional clinical data at biopsy provides earlier risk prediction in IgAN than current best methods that use 2 years of follow-up data. We used a cohort of 901 adults with IgAN from the Oxford derivation and North American validation studies and the VALIGA study followed for a median of 5.6 years to analyze the primary outcome (50% decrease in eGFR or ESRD) using Cox regression models. Covariates of clinical data at biopsy (eGFR, proteinuria, MAP) with or without MEST, and then 2-year clinical data alone (2-year average of proteinuria/MAP, eGFR at biopsy) were considered. There was significant improvement in prediction by adding MEST to clinical data at biopsy. The combination predicted the outcome as well as the 2-year clinical data alone, with comparable calibration curves. This effect did not change in subgroups treated or not with RAS blockade or immunosuppression. Thus, combining the MEST score with cross-sectional clinical data at biopsy provides earlier risk prediction in IgAN than our current best methods.

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In this study, patients treated with cetuximab and radiotherapy, showing high baseline of both ADCC and EGFR3+, have significant higher probability of achieving a complete response and a long overall survival compared to the others.

Background The VALidation of IGA (VALIGA) study investigated the utility of the Oxford Classification of immunoglobulin A nephropathy (IgAN) in 1147 patients from 13 European countries. Methods. Biopsies were scored by local pathologists followed by central review in Oxford. We had two distinct objectives: to assess how closely pathology findings were associated with the decision to give corticosteroid/immunosuppressive (CS/IS) treatments, and to determine the impact of differences in MEST-C scoring between central and local pathologists on the clinical value of the Oxford Classification. We tested for each lesion the associations between the type of agreement (local and central pathologists scoring absent, local present and central absent, local absent and central present, both scoring present) with the initial clinical assessment, as well as long-term outcomes in those patients who did not receive CS/IS. Results All glomerular lesions (M, E, C and S) assessed by local pathologists were independently associated with the decision to administer CS/IS therapy, while the severity of tubulointerstitial lesions was not. Reproducibility between local and central pathologists was moderate for S (segmental sclerosis) and T (tubular atrophy/interstitial fibrosis), and poor for M (mesangial hypercellularity), E (endocapillary hypercellularity) and C (crescents). Local pathologists found statistically more of each lesion, except for the S lesion, which was more frequent with central review. Disagreements were more likely to occur when the proportion of glomeruli affected was low. The M lesion, assessed by central pathologists, correlated better with the severity of the disease at presentation and discriminated better with outcomes. In contrast, the E lesion, evaluated by local pathologists, correlated better with the clinical presentation and outcomes when compared with central review. Both C and S lesions, when discordant between local and central pathologists, had a clinical phenotype intermediate to double absent lesions (milder disease) and double present (more severe). Conclusion We conclude that differences in the scoring of MEST-C criteria between local pathologists and a central reviewer have a significant impact on the prognostic value of the Oxford Classification. Since the decision to offer immunosuppressive therapy in this cohort was intimately associated with the MEST-C score, this study indicates a need for a more detailed guidance for pathologists in the scoring of IgAN biopsies.

PTDB supplies reliable data on the actual kidney state, with better results for needle biopsy. Although the biopsy size plays a role, samples with over 10 glomeruli suffice for clinical purposes. Vascular damage is the most faithful single parameter, whereas global glomerulosclerosis estimation requires some caution.

Objectives: within this investigation we investigated several approaches to enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection Methods: the investigation was carried out within the SOS clinical trial (NCT03645018) for lung cancer screening with DTS. Lung nodules were identified by visual analysis and then classified using the diameter and the radiological aspect of the nodule following a modified lung-RADS classification.Haralick texture features were extracted from the segmented nodules. Both semantic variables and radiomics features were used to build a predictive model using two approaches: logistic regression model on a sub-set of variables selected with backward feature selection or machine learning using the whole sub-set of variables. We used two machine learning methods: a Random Forest and a neural network. Machine learning methods were applied to a training set and validated on a test set. Methods were compared using diagnostic accuracy metrics.Results: binary visual analysis had a good sensitivity (0.95) but a low PPV (0.14). Lung-RADS classification increased the PPV (0.19) but with an unacceptable low sensitivity (0.65). Analogously, logistic regression showed a mildly increased PPV (0.22) and a low sensitivity (0.67). Random Forest demonstrated a low accuracy with a moderate PPV (0.40) but with a dramatically low sensitivity (0.30). Neural network demonstrated to be the best predictor with a nearly perfect PPV (0.95) and a high sensitivity (0.90).Conclusions: among the various technique to reduce the false positive rates of DTS the neural network demonstrated a very high PPV. The use of visual analysis along with neural network could help radiologists to depict a follow-up strategy after a positive DTS.

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