CD30+ Anaplastic Large Cell Lymphoma

We collected data from electronic patient files. Two different researchers (E.A.M.Z. and L.A.W.) collected data independently. Uncertainties were discussed together and, if necessary, with the other researchers (A.B., R.J.N.) to reach a consensus. Due to the retrospective design, no standard formats were used for the description of the investigated variables. For example, involvement of the body regions was scored based on the radiology reports. Sometimes, one of the investigators was not certain how to interpret the radiology reports. This was discussed with the colleagues, and if necessary, the radiology investigations were re-analyzed by the involved radiologist of our study.
Data were processed anonymously and encrypted.
We identified predicting factors for lymphoma based on an extensive search of the literature using PubMed, Medline, and Embase. We searched for studies using Medical Subject Heading terms including “lymphadenopathy”, “child”, “adolescent”, and “lymphoma”. An overview of potential predicting factors based on this search of the literature and their results are given in Table S1 [8 (link),31 (link),32 (link),33 (link),34 (link),35 (link),36 (link),37 (link),38 (link),46 ,47 (link),48 (link),49 (link),50 (link),51 (link),52 (link)]. We identified 39 potential predictors and included these in our univariate analyses: age, gender, presence of B-symptoms, 11 laboratory parameters including TARC, and several imaging findings. These variables and their definitions are listed in Table S2.
The body regions of the involved areas were scored individually. An overview of the separately scored anatomical body regions and an explanation is provided in Table S3.
We used pathology reports primarily for defining the diagnosis; 158 out of 182 patients underwent biopsy, including all cases of lymphoma. Twenty-four patients were diagnosed without a biopsy, but based on clinical, radiological, microbiological, and laboratory results (twenty infectious/reactive lymphadenopathy, one venous malformation, one lymphangioma, one branchiogenic cyst, and one dermoid cyst).
We categorized the patients into 12 groups according to their diagnosis. The malignant diagnoses in the study population included: cHL, NLPHL, ALCL, primary mediastinal large B-cell lymphoma (PMBCL), diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma (BL), T-LBL, B-cell lymphoblastic lymphoma (B-LBL), and other malignancies (Langerhans cell histiocytosis (LCH)). Furthermore, there were three groups with benign causes of lymphadenopathy: reactive or infectious lymphadenopathy, progressive transformation of germinal centers (PTGC), and other non-malignant causes.
For the identification of predictive factors, we divided the outcome into the benign group and the malignant group for univariate analysis. However, the malignant group contained nine different diagnoses, which differ significantly in incidence and clinical presentation. Therefore, we subdivided the group into five categories for multivariate analysis: cHL, NLPHL, NHL, other malignancies, and the benign group. In brief, we collected data from electronic patient files. We identified 39 potential predictors based on an extensive search of the literature. We used pathology reports for defining the diagnosis.

None of the 1D RCTE models capture the 4 µm absorption feature seen in the data. We searched for several candidate gas species that could produce this feature if their abundances differ from the expected abundances from thermochemical equilibrium. The list of searched chemical species include C-bearing gases such as C₂H₂, CS, CS₂, C₂H₆, C₂H₄, CH₃, CH, C₂, CH₃Cl, CH₃F, CN and CP. Various metal hydrides, bromides, flourides and chlorides such as LiH, AlH, FeH, CrH, BeH, TiH, CaH, HBr, LiCl, HCl, HF, AlCl, NaF and AlF were also searched as potential candidates to explain the feature. SO₂, SO₃, SO and SH are among the sulfur-based gases that were considered. Other species that were considered include gases such as PH₃, H₂S, HCN, N₂O, GeH₄, SiH₄, SiO, AsH₃, H₂CO, H⁺₃, OH⁺, KOH, Brα-H, AlO, CN, CP, CaF, H₂O₂, H₃O⁺, HNO₃, KF, MgO, PN, PO, PS, SiH, SiO2, SiS, TiO and VO.
Among all these gases, SO₂ was the most promising candidate in terms of its spectral shape and chemical plausibility, although the expected chemical equilibrium abundance of SO₂ is too low to produce the absorption signal seen in the data. However, previous work exploring photochemistry in exoplanetary atmospheres^{25 (link),26 (link)} have shown that higher amounts of SO₂ can be created in the upper atmospheres of irradiated planets through photochemical processes. Therefore, we postprocess the PICASO 3.0 and ScCHIMERA chemical equilibrium models with varying amounts of SO₂ in a Bayesian framework to estimate the SO₂ abundance required to explain the strength of the 4-µm feature. The required volume mixing ratio of SO₂ was found to be roughly 10⁻⁵–10⁻⁶. Note that in obtaining this estimate we assumed that the SO₂ volume mixing ratio does not vary with pressure for simplicity. In a photochemical scenario this assumption is probably not realistic, although the pressure range probed by SO₂ is also limited. Whether photochemical models can produce this amount of SO₂ in the atmospheric conditions of WASP-39b is a pressing question that the ERS team is now exploring (Welbanks et al. (in prep), Tsai et al. (submitted)). Whether this feature can be better explained by any other gaseous absorber is also at present under investigation by the ERS team.