SummaryThe management of acute myeloid leukaemia (AML) in India remains a challenge. In a two‐year prospective study at our centre there were 380 newly diagnosed AML (excluding acute promyelocytic leukaemia, AML‐M3) patients. The median age of newly diagnosed patients was 40 years (range: 1–79; 12·3% were ≤ 15 years, 16·3% were ≥ 60 years old) and there were 244 (64·2%) males. The median duration of symptoms prior to first presentation at our hospital was 4 weeks (range: 1–52). The median distance from home to hospital was 580 km (range: 6–3200 km). 109 (29%) opted for standard of care and were admitted for induction chemotherapy. Of the 271 that did not take treatment the major reason was lack of financial resources in 219 (81%). There were 27 (24·7%) inductions deaths and of these, 12 (44·5%) were due to multidrug‐resistant gram‐negative bacilli and 12 (44·5%) showed evidence of a fungal infection. The overall survival at 1 year was 70·4% ± 10·7%, 55·6% ± 6·8% and 42·4% ± 15·6% in patients aged ≤15 years, 15 ‐ 60 years and ≥60 years, respectively. In conclusion, the biggest constraint is the cost of treatment and the absence of a health security net to treat all patients with this diagnosis.

This study highlights the importance of evaluating expression of candidate Ara-C metabolizing genes in predicting ex vivo drug response as well as treatment outcome. RI could be a predictor of ex vivo Ara-C response irrespective of cytogenetic and molecular risk groups and a potential biomarker for AML treatment outcome and toxicity. Original submitted 22 December 2014; Revision submitted 9 April 2015.

Adolescent and young adult (AYA) patients with acute lymphoblastic leukaemia (ALL) have inferior survival when compared to children. The causes are multiple and include bad biology, differences in treatment approaches, and other complex social, economic and psychological factors that affect therapy adherence. 1 Intensive 'paediatric' regimens improve outcomes, but these come with the cost of higher toxicity, which may even negate these benefit of reduced relapse. 2-5 To understand the real-world data from India, we analysed the outcomes of AYA ALL (aged 15-29 years, treated between 2012 and 2017) from a retrospective database maintained by the Hematology Cancer Consortium (HCC). Baseline data of all patients (including those who were not treated) diagnosed within the period stipulated by a particular centre were captured, including reasons for not availing treatment. Survival outcomes were estimated for treated patients (censored on 31 July 2019). For this analysis, 'high risk' was defined based on white blood cell count (WBC) at diagnosis (B cell >30 9 10 9 /l, T cell >100 9 10 9 /l). Protocols such as Multicentre protocol 841 (MCP-841), Berlin-Frankfurt-M€ unster 95 (BFM-95), BFM-90, and Children's Oncology Group (COG) were considered 'paediatric type', whereas German Multicentre ALL (GMALL), hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone (Hyper-CVAD), and UKALL were considered 'adult type'. Minimal residual disease (MRD) >0Á01% (when assessed by flow cytometry) was considered positive. Of the 1383 patients registered, 1141(82Á5%) underwent treatment (Supplementary Table S1 and S2, baseline characteristics), and 242 did not start treatment (Fig 1). The inability to afford treatment was the commonest cause for not initiating treatment (105/1383, 7Á6%). There were no Fig 1. Flowchart depicting the outcomes of patients who were included in the registry. Of the 1383 patients, only 1141 started therapy (induction) and 863 (76%) achieved complete remission (CR). At last follow-up, 574 were in CR and on follow-up. A total of 336/1383 (24%) patients either did not start therapy (N = 242), or abandoned therapy after starting induction (N = 94) (A). (B) Comparison of induction outcomes between those treated with 'paediatric' and 'adult' protocols. There were no differences in terms of achievement of CR (76% vs. 73%, P = 0Á509), induction mortality (4Á7% vs. 3Á2%, P = 0Á842), or minimal residual disease (MRD) positivity rate (36% vs. 42%, P = 0Á382). (C) The commonest cause of induction mortality was infection (56%) followed by progressive disease (23%).

Background: Remission induction is the most intensive phase of acute myeloid leukemia (AML) treatment, associated with significant morbidity and mortality. Collaborative research and advances in supportive care have steadily improved outcomes in developed countries with induction mortality less than 5%. Challenges for treatment in resource limited settings are varied including delayed presentation, higher disease burden, baseline infections and poor general condition precluding standard intensive therapy, higher rates of resistant infections, and several social and financial constraints. Consequently, a significant proportion of patients do not receive definitive therapy and for those who are treated there is a considerably high risk of induction mortality. In an attempt to identify the subset of patients with highest risk of death during induction, we have developed a multivariate model of induction mortality score using baseline features relevant to our clinical setting by utilizing Indian acute leukemia research database [INwARD] established in 2018 by Hematology Cancer Consortium (HCC). Method: Retrospective data from January 2018 to May 2019 for adult AML was collected from 11 member institutions in a central online data management system. Selection of potential variables that would predict mortality was based on clinical and statistical significance. Thus, 10 variables defining baseline patient and disease characteristics (age, ECOG performance status, duration of symptoms in days, albumin, creatinine, bilirubin, white cell count, platelet, peripheral blood blast percentage, and presence of infection requiring intravenous antibiotic within one week prior of starting induction), were considered for the predictive model using machine learning algorithms: Logistic regression (LR) and Support Vector Machine (SVM). SVM was chosen as the best algorithm based on the AUCs. In order to get robust threshold, sensitivity, specificity and predictive values bootstrapping was done 10,000 times. The final statistics were based on the mean (SD) of bootstrapped sample. R software was used to bootstrap and analyze the data. Result: Of the 611 adult AML cases available during study period, 392 treated with the intensive '3+7' or its abbreviated regimen were considered for analysis. Median age of this cohort was 36 years (range 18 - 67), male to female ratio 1.34. European Leukemia Net (ELN) risk group distribution is shown in Fig 1a. Complete remission was attained in 52.8%. Induction mortality was 16.9 % ranging from 6.1% to 43% across different centers. Most common cause of death was infection (66.7%). Multi-drug resistant blood stream infection was documented in 25.4% cases. For the SVM model for predicting induction mortality using 10 covariates, the AUC based on the bootstrap validation was 91.3% with the best threshold probability being 0.262 (Fig 1b). Thus, a cut off score of 0.262 in the SVM model predicted induction death with sensitivity of 93.6% and specificity of 87.7%. Performance of each variable in the SVM model is shown in Fig 1c and comparison of the LR and SVM model in Fig 1d. Conclusion: Score predicting induction death with high accuracy will be a valuable tool in guiding clinicians against the use of intensive induction therapy, in tailoring of treatment as per individual patients' risk and proper resource allocation. Despite the limitations of retrospective data, wide disparity in resources, patient profile and treatment costs across centers accounting for variability in mortality rates, this study represents one major attempt to find answer to a locally relevant clinical problem of high induction mortality in a cohort of young adult AML, utilizing contemporary pooled data through multi-center collaboration. Optimal cut off point for the score needs to be validated in independent patient cohorts and have to be re-calibrated periodically. Further, an online calculator is being designed on the HCC online system to work as ready reckoner for clinicians. Figure 1 a) ELN risk group distribution b) descriptive statistics of bootstrapped SVM Model c) performance of each covariate in SVM model d) ROC Curve: Comparison of LR and SVM method Figure 1 Disclosures No relevant conflicts of interest to declare.

This phase 1-2 trial investigated the use of a 100% wt/vol emulsion of perfluorooctylbromide (PFOB) in computed tomography (CT) of 30 patients with metastatic cancer. Injection of 3 g/kg (maximum dose administered to these patients) provided an average liver enhancement of +31 HU on CT scans obtained after 48 hours. Maximum splenic opacification occurred immediately after injection; 1 g/kg, which allowed an immediate enhancement of +35 HU, appeared sufficient for the diagnosis of splenic conditions. Vascular opacification was insufficient for diagnostic purposes. In four patients with metastases, more lesions were seen with the use of PFOB with CT than with conventional CT. Adverse effects included five cases of low back pain that were reversible when the infusion rate was reduced. Fever and trembling were also noted 6 hours after injection in five patients. In all patients, symptoms regressed spontaneously within several hours. Clinically inapparent and dose-independent splenomegaly (volume increase of at least 20% on CT examinations) was noted in eight patients.