Erickson, Bradley J.

2014 Background: PFS6 is low (2.3%) and med OS brief (4 mos) for recurrent GBM pts who progress after prior RT/TMZ and bevacizumab (Bev), and then receive non-Bev containing regimens (Reardon et al, Br J Canc 2012). Following VEGFi exposure, tumor cell resistance potentially may result from activation of Ras/Raf/MEK/ERK (MAPK) and PI3K/Akt/mTOR signaling pathways. Temsirolimus (mTOR inhibitor) and sorafenib (Raf, PDGFR, VEGFR inhibitor) have previously shown limited single agent activity in GBM. Methods: Recurrent GBM pts who showed radiographic progression despite prior surgery, RT + temozolomide, and VEGFi (initially or at recurrence) received sorafenib 200 mg PO bid and temsirolimus 20 mg IV weekly (the NCCTG N0572 phase I MTD) until progression. All pts had received < 2 prior chemo regimens. A two-stage design was used, with 90% power to detect an increase in PFS6 from 8% to 23%, requiring 41 evaluable pts. The primary endpoint was PFS6; secondary endpoints included OS, TTP, and ORR. Toxicity (TOX) was assessed using CTC ver 3.0. Results: 44 evaluable pts were accrued. Four of the first 40 (10%) reached PFS6, but this did not meet the pre-study threshold for success. Median TTP was 1.8 mos. (40 events, 95% CI: 1.5 – 2.3); median OS was 5 mos. (36 events, 95% CI: 6.6 – 3.1), and no objective responses were reported. Eight pts are alive; 4 have not progressed (median F/U 3.1 mos.; max, 12.2 mos.). Grade 3+ non-heme AE were observed in 51% (23/45; fatigue-5, hypophosphatemia-7, hypercholesterolemia-5, hypertriglyceridemia-5). One grade 4 hematologic AE (thrombocytopenia) occurred. 13.3% (6/45 pts) went off due to TOX, and 71.1% (32/45) due to disease progression. Conclusions: Sorafenib plus temsirolimus was tolerable, but the primary endpoint threshold for success (PFS6) was not met in post-VEGFi recurrent GBM patients. Nevertheless, PFS6 (10%) and OS (5 mos.) compared favorably to contemporary series of Bev-refractory, recurrent GBM pts who are subsequently treated with a non-Bev containing regimen. Supported by NCI CA-25224. Clinical trial information: NCT00329719.

Significance Statement Because renal function often remains stable in the early stages of autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) is the most important biomarker of disease severity and progression. The authors developed and validated a fully automated method of simultaneously measuring TKV and total liver volume (TLV) as well as their growth rates from magnetic resonance images. Their findings indicate that the method’s performance is equivalent to the gold standard of manual tracing while requiring a fraction of the time and skill. This approach may address the unmet need for rapid and reliable assessment of TKV and TLV, allowing identification of patients with rapidly progressing ADPKD for emerging drug treatments and serving as a reliable alternative for assessment of disease progression in clinical trials. Background The formation and growth of cysts in kidneys, and often liver, in autosomal dominant polycystic kidney disease (ADPKD) cause progressive increases in total kidney volume (TKV) and liver volume (TLV). Laborious and time-consuming manual tracing of kidneys and liver is the current gold standard. We developed a fully automated segmentation method for TKV and TLV measurement that uses a deep learning network optimized to perform semantic segmentation of kidneys and liver. Methods We used 80% of a set of 440 abdominal magnetic resonance images (T2-weighted HASTE coronal sequences) from patients with ADPKD to train the network and the remaining 20% for validation. Both kidneys and liver were also segmented manually. To evaluate the method’s performance, we used an additional test set of images from 100 patients, 45 of whom were also involved in longitudinal analyses. Results TKV and TLV measured by the automated approach correlated highly with manually traced TKV and TLV (intraclass correlation coefficients, 0.998 and 0.996, respectively), with low bias and high precision (<0.1%±2.7% for TKV and −1.6%±3.1% for TLV); this was comparable with inter-reader variability of manual tracing (<0.1%±3.5% for TKV and −1.5%±4.8% for TLV). For longitudinal analysis, bias and precision were <0.1%±3.2% for TKV and 1.4%±2.9% for TLV growth. Conclusions These findings demonstrate a fully automated segmentation method that measures TKV, TLV, and changes in these parameters as accurately as manual tracing. This technique may facilitate future studies in which automated and reproducible TKV and TLV measurements are needed to assess disease severity, disease progression, and treatment response.