Comparison of the chromosomal pattern of primary testicular nonseminomas and residual mature teratomas after chemotherapy

About 70 to 75% of patients with nonseminomatous testicular germ cell tumors (NSs) present with metastases. When these metastases are treated with chemotherapy, often residual mature teratoma (RMT) is left. RMT is composed of fully differentiated somatic tissue. Untreated metastases of NSs rarely consist exclusively of mature somatic tissue. Apparently, after chemotherapy treatment there is a shift towards higher degrees of differentiation. Investigating tumor progression and the mechanism(s) involved in therapy-related differentiation, we compared the cytogenetically abnormal karyotypes of a series of 70 NSs with those of 31 RMTs. In NSs and RMTs, the modal total chromosome number does not differ and is in the triploid range. Both the frequency and the average copy number of i(12p) are the same, and the pattern of chromosomal over-and underrepresentation and distribution of breakpoints do not differ significantly in these series. So, we found the chromosomal pattern of RMTs as abnormal as those of primary NSs. Based on cytogenetics, we found no indication that specific chromosomal alterations parallel metastasis and therapy-related differentiation of the metastases. The cytogenetic data suggest that both induction of differentiation of (selected) cells or selection of cells with capacity to differentiate are possible mechanisms for the therapy-related differentiation of RMTs. (C) Elsevier Science Inc., 1997

Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma

We investigated whether the outcome prediction of patients with aggressive B-cell lymphoma can be improved by combining clinical, molecular genotype, and radiomics features. MYC, BCL2, and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline positron emission tomography–computed tomography of 323 patients, which included maximum standardized uptake value (SUV(max)), SUV(peak), SUV(mean), metabolic tumor volume (MTV), total lesion glycolysis, and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of (1) International Prognostic Index (IPI); (2) IPI plus MYC; (3) IPI, MYC, and MTV; (4) radiomics; and (5) MYC plus radiomics models were tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPVs). IPI yielded a CV-AUC of 0.65 ± 0.07 with a PPV of 29.6%. The IPI plus MYC model yielded a CV-AUC of 0.68 ± 0.08. IPI, MYC, and MTV yielded a CV-AUC of 0.74 ± 0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUV(peak) between 2 lesions, and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77 ± 0.07. The same radiomics features were retained when adding MYC (CV-AUC, 0.77 ± 0.07). PPV was highest for the MYC plus radiomics model (50.0%) and increased by 20% compared with the IPI (29.6%). Adding radiomics features improved model performance and PPV and can, therefore, aid in identifying poor prognosis patients