Immunogenicity and Immunosensitivity of Urethane-induced Murine Lung Adenomata, in Relation to the Immunological Impairment of the Primary Tumour Host

The depression of the immunological status of BALB/c mice treated during infancy with two different doses of urethane, alone or combined with cortisone, was evaluated by counting the number of plaque forming cells at 30 or 50 days of age. The incidence of lung adenomatous nodules was directly related to the degree of immunological impairment at 50 days of age. Twenty-seven lung adenomata were tested in an in vitro system involving spleen cells immune against the same single tumour used as target cell. Eighty-six per cent of tumours in the most immunodepressed group of mice were positive compared with 20-40% in the less immunodepressed groups. Syngeneic cross-reaction tests showed that non-immunogenic tumours were immunosensitive since 66% positive tests were obtained when target cells belonging to the less immunodepressed groups were tested with spleen cells of mice immunized with immunogenic adenomata

Prognosis based on primary breast carcinoma instead of pathological nodal status.

In breast cancer patients, prognostic information required to plan post-surgical therapy is obtained mainly through axillary dissection. This study was designed to establish a new prognostic score based solely on parameters of the primary tumour as an alternative to axillary surgery in assessing prognosis. Eight different prognostic factors, including menopausal status, tumour size, grading, lymphatic invasion, desmoplasia, necrosis, c-erbB-2 and laminin receptor expression, were evaluated retrospectively on a large series of primary breast carcinoma patients. From multivariate analysis, four independent parameters were selected and examined, alone and in combination, for their prognostic potential. These parameters were used to generate a prognostic score that was analysed retrospectively in 467 N0-N1a patients to determine its predictive value for survival. The score, which includes variables such as tumour size, grading, laminin receptor and c-erbB-2 overexpression, was established based on the number of negative prognostic factors: score 1 refers to cases in which all four parameters reflect a good prognosis, scores 2 and 3 refer to tumours in which, respectively, one or two of the four parameters reflect a poor prognosis, whereas score 4 refers to tumours with three or four poor prognosis factors. Analysis of the overall survival of the four score groups shows that patients with score 1 tumours (22% of the total) had the best prognosis with a 15 year survival of 82%, patients with score 2 and 3 had an intermediate prognosis, whereas score 4 patients had the poorest prognosis with a 15 year survival of only 38%. Moreover, survival in the N+ score 1 cases was found to be longer than that in the total N- patients. Our data suggest that the primary tumour score provides more reliable prognostic information than pathological nodal status, and that axillary dissection can be avoided in a large number of patients

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study

Introduction Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: Favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. Methods and analytics Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns' and mothers' clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. Ethics and dissemination This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study