A Prospective Phase II Study of Simultaneous Modulated Accelerated Radiotherapy Concurrently With CDDP/S1 for Esophageal Squamous Cell Carcinoma in the Elderly

BackgroundTo explore the efficacy and toxicity of simultaneous modulated accelerated radiotherapy (SMART) concurrently with cisplatin (CDDP) and S1 (tegafur/gimeracil/oteracil) in elderly patients with esophageal squamous cell carcinoma (ESCC).MethodsThis single-arm, phase II study enrolled pathologically confirmed, stage II–IVa ESCC of 70–80 years old and Eastern Cooperative Oncology Group performance status (ECOG PS) 0–2. Patients received SMART (64 Gy to gross tumor volume and 48 Gy to clinical target volume in 30 fractions) with concurrent CDDP (day 1 of each week) and S1 (days 1–14, 22–35). The primary endpoint was objective response rate (ORR). The secondary endpoints included progression-free survival (PFS), overall survival (OS) and toxicities.ResultsThirty-seven eligible patients were analyzed with median follow-up of 25.7 months for all and 46.1 months for survivors. The ORR was 88.9%. Patients with baseline weight loss <5% (p=0.050) and nutritional risk index (NRI) ≥105.2 (p=0.023) had better tumor response. Median PFS was 13.8 months with 2-year PFS of 37.5%. Median OS was 27.7 months with 2-year OS of 57.5%. OS was significantly associated with ECOG PS (p=0.005), stage (p=0.014), gross tumor volume (p=0.004), baseline NRI (p=0.036), baseline C-reactive protein (CRP) level (p=0.003) and tumor response (p=0.000). CRP level (p=0.016) and tumor response (p=0.021) were independently prognostic of OS. ≥grade 3 anemia, neutropenia and thrombocytopenia occurred in 2.7%, 10.8% and 13.5% of patients; ≥grade 3 esophagitis and pneumonitis occurred in 18.9% and 2.7% of patient, respectively.ConclusionSMART concurrently with CDDP/S1 yielded satisfactory response rate, survival outcome and tolerable treatment-related toxicities in elderly patients with ESCC. Further studies are warranted to validate the results.

Pattern of Recurrence of Glioblastoma Versus Grade 4 IDH-Mutant Astrocytoma Following Chemoradiation

Purpose To quantitatively compare the recurrence pattern of glioblastoma (IDH-wild type) versus grade 4 IDH-mutant astrocytoma (herein referred to as wtIDH and mutIDH, respectively) following primary chemoradiation. Methods Twenty-two wtIDH and 22 mutIDH patients matched by sex, extent of resection, and corpus callosum involvement were enrolled. The recurrent gross tumor volume (rGTV) was compared with both the gross tumor volume (GTV) and clinical target volume (CTV) from radiotherapy planning. Failure patterns were quantified by the incidence and volume of the rGTV outside the GTV and CTV, and positional differences of the rGTV centroid from the GTV and CTV. Results The GTV was smaller in wtIDH compared to the mutIDH group (mean±SD: 46.5±26.0 cm3 v. 72.2±45.4 cm3, p=0.026). The rGTV was 10.7±26.9 cm3 and 46.9±55.0 cm3 smaller than the GTV for the same groups (p=0.018). The rGTV extended outside the GTV in 22 (100%) and 15 (68%) (p=0.009) of wtIDH and mutIDH patients, respectively; however, the volume of rGTV outside the GTV was not significantly different (12.4±16.1 cm3 vs. 8.4±14.2 cm3, p=0.443). The rGTV metrics extending outside the CTV was not different between the groups. The rGTV centroid was within 5.7 mm of the closest GTV edge for 21 (95%) and 22 (100%) of wtIDH and mutIDH patients, respectively. Conclusion The rGTV extended beyond the GTV less often in mutIDH patients, suggesting limited margin radiotherapy could be beneficial in this group. The results support the study of small margin adaptive radiotherapy per the ongoing UNITED MR-Linac 5 mm CTV trial (NCT04726397).

NIMG-62. 68(GA)DOTATATE PET-BASED RADIATION CONTOURING CREATES SMALLER AND MORE PRECISE RADIATION VOLUMES FOR MENINGIOMA PATIENTS

PURPOSE Radiation treatment planning for meningiomas conventionally involves MRI contrast enhanced images to define residual tumor. However, the gross tumor volume may be difficult to delineate for patients with a meningioma in the skull base, sagittal sinus, or post resection. Advanced PET imaging using 68(GA)DOTATATE PET, which has been shown to be more sensitive and specific than MRI imaging, can be used for target volume delineation in these circumstances. We hypothesize that 68(GA)DOTATATE PET scan-based treatment planning will lead to smaller radiation volumes and will detect additional areas of disease compared to standard MRI alone. METHODS Our data evaluated retrospective, deidentified, and blinded gross tumor volume (GTV) contour delineation with 7 CNS specialists (3 neuroradiologists, 4 CNS radiation oncologists) for 26 patients diagnosed with a meningioma who received both a 68(GA)DOTATATE PET and an MRI for radiation treatment planning. Both the MRI and the PET were non-sequentially contoured by each physician for each patient. RESULTS The mean MRI volume for each physician ranged from 24.14-35.52 ccs. The mean PET volume for each physician ranged from 10.59-20.54 ccs. The PET volumes were significantly smaller for 6 out of the 7 physicians. In addition, 7/26 (27%) patients had new non-adjacent areas contoured on PET by at least 6 of the 7 physicians that were not contoured by these physicians on the corresponding MRI. These new areas would not have been in the traditional MRI based volumes. CONCLUSION Our study supports that 68(GA)DOTATATE PET imaging can help radiation oncologist create smaller and more precise radiation treatment volumes. Utilization of 68(GA)DOTATATE PET may find undetected areas of disease which in turn can improve local control and progression free survival. 68(GA)DOTATATE PET guided treatment planning should be studied prospectively.

Tumor Stage-Based Gross Tumor Volume of Resectable Esophageal Squamous Cell Carcinoma Measured on CT: Association With Early Recurrence After Esophagectomy

ObjectiveTo investigate relationship of tumor stage-based gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) measured on computed tomography (CT) with early recurrence (ER) after esophagectomy.Materials and MethodsTwo hundred and four consecutive patients with resectable ESCC including 159 patients enrolled in the training cohort (TC) and 45 patients in validation cohort (VC) underwent contrast-enhanced CT less than 2 weeks before esophagectomy. GTV was retrospectively measured by multiplying sums of all tumor areas by section thickness. For the TC, univariate and multivariate analyses were performed to determine factors associated with ER. Mann-Whitney U test was conducted to compare GTV in patients with and without ER. Receiver operating characteristic (ROC) analysis was performed to determine if tumor stage-based GTV could predict ER. For the VC, unweighted Cohen’s Kappa tests were used to evaluate the performances of the previous ROC predictive models.ResultsER occurred in 63 of 159 patients (39.6%) in the TC. According to the univariate analysis, histologic differentiation, cT stage, cN stage, and GTV were associated with ER after esophagectomy (all P-values < 0.05). Multivariate analysis revealed that cT stage and GTV were independent risk factors with hazard ratios of 3.382 [95% confidence interval (CI): 1.533–7.459] and 1.222 (95% CI: 1.125–1.327), respectively (all P-values < 0.05). Mann-Whitney U tests showed that GTV could help differentiate between ESCC with and without ER in stages cT1-4a, cT2, and cT3 (all P-values < 0.001), and the ROC analysis demonstrated the corresponding cutoffs of 13.31, 17.22, and 17.83 cm3 with areas under the curve of more than 0.8, respectively. In the VC, the Kappa tests validated that the ROC predictive models had good performances for differentiating between ESCC with and without ER in stages cT1-4a, cT2, and cT3 with Cohen k of 0.696 (95% CI, 0.498–0.894), 0.733 (95% CI, 0.386–1.080), and 0.862 (95% CI, 0.603–1.121), respectively.ConclusionGTV and cT stage can be independent risk factors of ER in ESCC after esophagectomy, and tumor stage-based GTV measured on CT can help predict ER.

Volumetric staging in radiotherapy for oropharyngeal cancers

Introduction: Despite routine use of 3D radiotherapy planning in radical radio(chemo)therapy for oropharyngeal cancers, volumetric data have not been implemented in initial staging. We analyzed 228 oropharyngeal cancer cases treated at one institution between 2004 and 2014 to compare the predictive value of volumetric staging and tumor nodal metastasis staging system (TNM) and determine whether they could be complementary for the estimation of survival. Methods: This retrospective study analyzed 228 consecutive oropharyngeal cancer cases treated with radiotherapy (76.9%) or concurrent radiochemotherapy (23.1%) between 2004 and 2014. The volumetric parameters included primary gross tumor volume (pGTV), metastatic lymph nodes gross tumor volume (nGTV), and total gross tumor volume (tGTV), and were compared with the 7th edition of the TNM staging system. Results: Median overall survival (OS) was 30.3 months. In the receiver operating characteristic analysis, tGTV had the highest area under the curve (AUC) of 0.66, followed by pGTV (AUC,0.64), nGTV (AUC 0.62), and TNM (AUC 0.6). The median OS for patients with tGTV ⩽32.2 mL was 40.5 months, compared to 15.4 months for >32.2 mL ( p < 0.001). This threshold allowed for a statistically significant difference in survival between TNM stage IV cases with low and high tumor volume ( p < 0.001). Despite both TNM and tGTV reaching statistical significance in univariate analysis, only the tGTV remained an independent prognostic factor in the multivariate analysis (hazard ratio 1.07, confidence interval 1.02–1.12, p = 0.008). Conclusions: tGTV is an independent prognostic factor, characterized by a higher discriminatory value than the TNM staging system, and can be used to further divide stage IV cases into subgroups with significantly different prognosis.

Primary Gross Tumor Volume is Prognostic and Suggests Treatment in Upper Esophageal Cancer

Background: To aid clinicians strategizing treatment for upper esophageal squamous cell carcinoma (ESCC), this retrospective study investigated associations between primary gross tumor volume (GTVp) and prognosis in patients given surgical resection, radiotherapy, or both resection and radiotherapy. Methods: The population comprised 568 patients with upper ESCC given definitive treatment, including 238, 216, and 114 who underwent surgery, radiotherapy, or combined radiotherapy and surgery. GTVp as a continuous variable was entered into the multivariate Cox model using penalized splines (P-splines) to determine the optimal cutoff value. Propensity score matching (PSM) was used to adjust imbalanced characteristics among the treatment groups. Results: P-spline regression revealed a dependence of patient outcomes on GTVp, with 30 cm3 being an optimal cut-off for differences in overall and progression-free survival (OS, PFS). GTVp ≥ 30 cm3 was a negative independent prognostic factor for OS and PFS. PSM analyses confirmed the prognostic value of GTVp. For GTVp < 30 cm3, no significant survival differences were observed among the 3 treatments. For GTVp ≥ 30 cm3, the worst 5-year OS rate was experienced by those given surgery. The 5-year PFS rate of patients given combined radiotherapy and surgery was significantly better than that of patients given radiotherapy. The surgical complications of patients given the combined treatment were comparable to those who received surgery, but radiation side effects were significantly lower. Conclusion: GTVp is prognostic for OS and PFS in upper ESCC. For patients with GTVp ≥ 30 cm3, radiotherapy plus surgery was more effective than either treatment alone.

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets—the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.