Towards Uncovering the Role of Incomplete Penetrance in Maculopathies through Sequencing of 105 Disease-Associated Genes.

Inherited macular dystrophies (iMDs) are a group of genetic disorders, which affect the central region of the retina. To investigate the genetic basis of iMDs, we used single-molecule Molecular Inversion Probes to sequence 105 maculopathy-associated genes in 1352 patients diagnosed with iMDs. Within this cohort, 39.8% of patients were considered genetically explained by 460 different variants in 49 distinct genes of which 73 were novel variants, with some affecting splicing. The top five most frequent causative genes were ABCA4 (37.2%), PRPH2 (6.7%), CDHR1 (6.1%), PROM1 (4.3%) and RP1L1 (3.1%). Interestingly, variants with incomplete penetrance were revealed in almost one-third of patients considered solved (28.1%), and therefore, a proportion of patients may not be explained solely by the variants reported. This includes eight previously reported variants with incomplete penetrance in addition to CDHR1:c.783G>A and CNGB3:c.1208G>A. Notably, segregation analysis was not routinely performed for variant phasing-a limitation, which may also impact the overall diagnostic yield. The relatively high proportion of probands without any putative causal variant (60.2%) highlights the need to explore variants with incomplete penetrance, the potential modifiers of disease and the genetic overlap between iMDs and age-related macular degeneration. Our results provide valuable insights into the genetic landscape of iMDs and warrant future exploration to determine the involvement of other maculopathy genes.

SRSF1 haploinsufficiency is responsible for a syndromic developmental disorder associated with intellectual disability.

SRSF1 (also known as ASF/SF2) is a non-small nuclear ribonucleoprotein (non-snRNP) that belongs to the arginine/serine (R/S) domain family. It recognizes and binds to mRNA, regulating both constitutive and alternative splicing. The complete loss of this proto-oncogene in mice is embryonically lethal. Through international data sharing, we identified 17 individuals (10 females and 7 males) with a neurodevelopmental disorder (NDD) with heterozygous germline SRSF1 variants, mostly de novo, including three frameshift variants, three nonsense variants, seven missense variants, and two microdeletions within region 17q22 encompassing SRSF1. Only in one family, the de novo origin could not be established. All individuals featured a recurrent phenotype including developmental delay and intellectual disability (DD/ID), hypotonia, neurobehavioral problems, with variable skeletal (66.7%) and cardiac (46%) anomalies. To investigate the functional consequences of SRSF1 variants, we performed in silico structural modeling, developed an in vivo splicing assay in Drosophila, and carried out episignature analysis in blood-derived DNA from affected individuals. We found that all loss-of-function and 5 out of 7 missense variants were pathogenic, leading to a loss of SRSF1 splicing activity in Drosophila, correlating with a detectable and specific DNA methylation episignature. In addition, our orthogonal in silico, in vivo, and epigenetics analyses enabled the separation of clearly pathogenic missense variants from those with uncertain significance. Overall, these results indicated that haploinsufficiency of SRSF1 is responsible for a syndromic NDD with ID due to a partial loss of SRSF1-mediated splicing activity.

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.

PURPOSE: The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. MATERIALS AND METHODS: Image features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, were calculated from pre-treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (RadCT ), radiomics CT and BED (RadCT,BED ), deep learning (DL) CT (DLCT ), and DL CT and BED (DLCT,BED ). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5. RESULTS: Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a RadCT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75-0.85)) and AUC of 0.773 (0.688-0.846), and a DLCT,BED model also using three variables with an accuracy of 0.798 (0.649-0.829) and AUC of 0.812 (0.755-0.867). CONCLUSION: According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.

Inherited Retinal Diseases Due to RPE65 Variants: From Genetic Diagnostic Management to Therapy.

Inherited retinal diseases (IRDs) are a heterogeneous group of conditions that include retinitis pigmentosa (RP) and Leber congenital amaurosis (LCA) and early-onset severe retinal dystrophy (EO[S]RD), which differ in severity and age of onset. IRDs are caused by mutations in >250 genes. Variants in the RPE65 gene account for 0.6-6% of RP and 3-16% of LCA/EORD cases. Voretigene neparvovec is a gene therapy approved for the treatment of patients with an autosomal recessive retinal dystrophy due to confirmed biallelic RPE65 variants (RPE65-IRDs). Therefore, the accurate molecular diagnosis of RPE65-IRDs is crucial to identify 'actionable' genotypes-i.e., genotypes that may benefit from the treatment-and is an integral part of patient management. To date, hundreds of RPE65 variants have been identified, some of which are classified as pathogenic or likely pathogenic, while the significance of others is yet to be established. In this review, we provide an overview of the genetic diagnostic workup needed to select patients that could be eligible for voretigene neparvovec treatment. Careful clinical characterization of patients by multidisciplinary teams of experts, combined with the availability of next-generation sequencing approaches, can accelerate patients' access to available therapeutic options.

Prognostic Nutritional Index Predicts Toxicity in Head and Neck Cancer Patients Treated with Definitive Radiotherapy in Association with Chemotherapy.

BACKGROUND: The Prognostic Nutritional Index (PNI) is a parameter of nutritional and inflammation status related to toxicity in cancer treatment. Since data for head and neck cancer are scanty, this study aims to investigate the association between PNI and acute and late toxicity for this malignancy. METHODS: A retrospective cohort of 179 head and neck cancer patients treated with definitive radiotherapy with induction/concurrent chemotherapy was followed-up (median follow-up: 38 months) for toxicity and vital status between 2010 and 2017. PNI was calculated according to Onodera formula and low/high PNI levels were defined according to median value. Odds ratio (OR) for acute toxicity were calculated through logistic regression model; hazard ratios (HR) for late toxicity and survival were calculated through the Cox proportional hazards model. RESULTS: median PNI was 50.0 (interquartile range: 45.5-53.5). Low PNI was associated with higher risk of weight loss > 10% during treatment (OR = 4.84, 95% CI: 1.73-13.53 for PNI < 50 versus PNI ≥ 50), which was in turn significantly associated with worse overall survival, and higher risk of late mucositis (HR = 1.84; 95% CI:1.09-3.12). PNI predicts acute weight loss >10% and late mucositis. CONCLUSIONS: PNI could help clinicians to identify patients undergoing radiotherapy who are at high risk of acute and late toxicity.

Radiomics and deep learning in lung cancer.

Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.

Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis.

Purpose: to predict the occurrence of late subcutaneous radiation induced fibrosis (RIF) after partial breast irradiation (PBI) for breast carcinoma by using machine learning (ML) models and radiomic features from 3D Biologically Effective Dose (3D-BED) and Relative Electron Density (3D-RED). Methods: 165 patients underwent external PBI following a hypo-fractionation protocol consisting of 40 Gy/10 fractions, 35 Gy/7 fractions, and 28 Gy/4 fractions, for 73, 60, and 32 patients, respectively. Physicians evaluated toxicity at regular intervals by the Common Terminology Adverse Events (CTAE) version 4.0. RIF was assessed every 3 months after the completion of radiation course and scored prospectively. RIF was experienced by 41 (24.8%) patients after average 5 years of follow up. The Hounsfield Units (HU) of the CT-images were converted into relative electron density (3D-RED) and Dose maps into Biologically Effective Dose (3D-BED), respectively. Shape, first-order and textural features of 3D-RED and 3D-BED were calculated in the planning target volume (PTV) and breast. Clinical and demographic variables were also considered (954 features in total). Imbalance of the dataset was addressed by data augmentation using ADASYN technique. A subset of non-redundant features that best predict the data was identified by sequential feature selection. Support Vector Machines (SVM), ensemble machine learning (EML) using various aggregation algorithms and Naive Bayes (NB) classifiers were trained on patient dataset to predict RIF occurrence. Models were assessed using sensitivity and specificity of the ML classifiers and the area under the receiver operator characteristic curve (AUC) of the score functions in repeated 5-fold cross validation on the augmented dataset. Results: The SVM model with seven features was preferred for RIF prediction and scored sensitivity 0.83 (95% CI 0.80-0.86), specificity 0.75 (95% CI 0.71-0.77) and AUC of the score function 0.86 (0.85-0.88) on cross-validation. The selected features included cluster shade and Run Length Non-uniformity of breast 3D-BED, kurtosis and cluster shade from PTV 3D-RED, and 10th percentile of PTV 3D-BED. Conclusion: Textures extracted from 3D-BED and 3D-RED in the breast and PTV can predict late RIF and may help better select patient candidates to exclusive PBI.

Electron radiotherapy (IOERT) for applications outside of the breast: Dosimetry and influence of tissue inhomogeneities.

PURPOSE: The purpose of study is to investigate the dosimetry of electron intraoperative radiotherapy (IOERT) of the Intraop Mobetron 2000 mobile LINAC in treatments outside of the breast. After commissioning and external validation of dosimetry, we report in vivo results of measurements for treatments outside the breast in a large patient cohort, and investigate if the presence of inhomogeneities can affect in vivo measurements. METHODS AND MATERIALS: Applicator factors and profile curves were measured with a stereotactic diode. The applicators factors of the 6 cm flat and beveled applicators were also confirmed with radiochromic films, parallel-plate ion chamber and by an external audit performed with ThermoLuminescent Dosimeters (TLDs). The influence of bone on dose was investigated by using radiochromic films attached to an insert equivalent to cortical bone, immersed in the water phantom. In vivo dosimetry was performed on 126 patients treated with IOERT using metal oxide-silicon semiconductor field effect transistors (MOSFETs) placed on the tumor bed. RESULTS: Relatively small differences were found among different detectors for measurements of applicator factors. In the external audit, the agreement with the TLD was mostly within ±0.2%. The largest increase of dose due to the presence of cortical bone insert was +6.0% with energy 12 MeV and 3 cm applicator. On average, in vivo dose was significantly (+3.1%) larger than prescribed dose. CONCLUSION: IOERT in applications outside the breast results in low discrepancies between in vivo and prescribed doses, which can be also explained with the presence of tissue inhomogeneity.

Prediction of skin dose in low-kV intraoperative radiotherapy using machine learning models trained on results of in vivo dosimetry.

PURPOSE: The purpose of this study was to implement a machine learning model to predict skin dose from targeted intraoperative (TARGIT) treatment resulting in timely adoption of strategies to limit excessive skin dose. METHODS: A total of 283 patients affected by invasive breast carcinoma underwent TARGIT with a prescribed dose of 6 Gy at 1 cm, after lumpectomy. Radiochromic films were used to measure the dose to the skin for each patient. Univariate statistical analysis was performed to identify correlation of physical and patient variables with measured dose. After feature selection of predictors of in vivo skin dose, machine learning models stepwise linear regression (SLR), support vector regression (SVR), ensemble with bagging or boosting, and feed forward neural networks were trained on results of in vivo dosimetry to derive models to predict skin dose. Models were evaluated by tenfold cross validation and ranked according to root mean square error (RMSE) and adjusted correlation coefficient of true vs predicted values (adj-R2 ). RESULTS: The predictors correlated with in vivo dosimetry were the distance of skin from source, depth-dose in water at depth of the applicator in the breast, use of a replacement source, and irradiation time. The best performing model was SVR, which scored RMSE and adj-R2 , equal to 0.746 [95% confidence intervals (CI), 95% CI 0.737,0.756] and 0.481 (95% CI 0.468,0.494), respectively, on the tenfold cross validation. CONCLUSION: The model trained on results of in vivo dosimetry can be used to predict skin dose during setup of patient for TARGIT and this allows for timely adoption of strategies to prevent of excessive skin dose.

Voxel-by-voxel correlation between radiologically radiation induced lung injury and dose after image-guided, intensity modulated radiotherapy for lung tumors.

PURPOSE: To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy. METHODS AND MATERIALS: The follow-up CT scans from 32 lung cancer patients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361gcm-3 to the radiation dose. The model's predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (Ptest). RESULTS: The best-fit parameters for prediction of RRLI 6months post RT were D50 of 73.0 (95% CI 59.2.4-85.3.7)Gy, and m of 0.41 (0.39-0.46) for hypofractionated (5 and 8 fractions) and D50 of 96.8 (76.9-123.9)Gy, and m of 0.36 (0.34-0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575-0.583) for fractionated and 0.579 (0.577-0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the Ptest. CONCLUSIONS: The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients.

Image-guided volumetric arc radiotherapy of pancreatic cancer with simultaneous integrated boost: Optimization strategies and dosimetric results.

PURPOSE: To introduce volumetric modulated arc therapy treatments (VMAT) with simultaneous integrated boost (SIB) for pancreatic cancer and describe dosimetric results on a large patient series. METHODS AND MATERIALS: 45 patients with pancreatic malignancies were treated with 18 MV single-arc VMAT. Image guidance was performed with daily online kilo-volt cone-beam computed tomography (CBCT). The conformity index (CI) and homogeneity index (HI) to the target volumes, PTV45Gy and PTV54Gy, and dose-volume indices to OARs from the QUANTEC task group were reported. The risk of clinical nephritis was evaluated using normal tissue complication probability (NTCP). Treatments were verified in-phantom with the Delta4 system. RESULTS: Average CI was 1.06 with 95% confidence intervals (95% CI) of 0.97-1.22 for PTV45Gy and 1.17 (0.66-1.61) for PTV54Gy. HI of PTV54Gy was 1.06 (1.04-1.10). OAR constraints were achieved in all patients, except for kidneys V12Gy of 48 (35.4-72.3)%. NTCP of the kidneys was 0.98 (0.6-1.7)%. Kidneys V12Gy and V20Gy were inversely related to PTV54Gy CI and maximum dose. All in-phantom tests had gamma pass rates exceeding 95% with global 3% dose difference and 3 mm distance to agreement. Patient shifts measured with CBCT had 95% CI of -0.8, +0.8 in the RL, -0.7, +0.8 in the SI, and -0.8, +0.7 cm in the AP directions. CONCLUSIONS: Dosimetric results of VMAT were excellent on PTVs and organs at risk. The kidneys represent the dose-limiting organ at risk for this technique. NTCP indicates that this technique is safe from radiation-induced side effects to the kidneys.

Fiducial markers for image-guided partial breast irradiation.

PURPOSE: This study was undertaken to study the role of fiducial markers for image-guided partial breast irradiation (IG-PBI), and to compare the shifts based on bony anatomy and fiducial markers. MATERIALS AND METHODS: Fifteen patients underwent IGPBI. Three fiducial markers were placed in the tumour bed at the time of surgery. Daily orthogonal anterior/ posterior and lateral kV-images were taken before each fraction and compared with the digitally-reconstructed radiographs, both using bony landmarks and fiducial markers as reference. The Student's t test was used to detect a meaningful difference of 3 mm in between the two methods. RESULTS: A total of 105 image-guided radiation therapy (IGRT) sessions were obtained. The mean superior/inferior, right/left, and anterior/posterior shifts obtained using the bony landmarks vs. the fiducial markers were 2 mm [standard deviation (SD) 10 mm] vs. 0 mm (SD 7 mm), 0 mm (SD 7 mm) vs. 1 mm (SD 4 mm), and 1 mm (SD 7 mm) vs. 0 mm (SD 5 mm), respectively. The mean shift differences in absolute value between the two methods, along the superior/inferior, right/left and anterior/posterior directions were 5 mm (p=0.001), 3 mm [p=not significant (ns)], and 3 mm (p=ns), respectively. CONCLUSIONS: Fiducial markers for IG-PBI increase set-up accuracy compared to the bony landmarks, in particular along the superior/inferior direction.

Dose to the skin in helical tomotherapy: results of in vivo measurements with radiochromic films.

PURPOSE: The aim of this study is to report results of measurements of dose to the skin in vivo with radiochromic EBT films in treatments with helical tomotherapy. METHODS AND MATERIALS: In vivo measurements were performed by applying pieces of radiochromic films to the skin or to the inner side of thermoplastic mask before the treatment. The sites of treatment included scalp, brain, head and neck, cranio-spinal axis and lower limbs. Skin dosimetry was performed in a patient who experienced grade 3-4 acute side effects to the skin shortly after the first treatment sessions. For each patient we measured the setup errors using the daily MVCT acquired for image guidance of the treatment. EBT films were read with a flatbed Epson Expression scanner and images were processed with an in-house written routine. RESULTS: A total of 96 measurements of dose to the skin performed on 14 patients. The mean difference and standard error of the mean difference between measured and TPS-calculated dose was -9.2% ± 2.6% for all treatments, -6.6% ± 2.6% for head and neck treatments. These differences were statistically significant at the 0.05 significance level (t-Student test). Planned dose and dose range in the region of measurements were not correlated with dose discrepancy. CONCLUSIONS: Radiochromic EBT films are suitable detectors for surface dose measurements in tomotherapy treatments. Results show that TPS overestimates dose to the skin measured with EBT radiochromic films. In vivo skin measurements with EBT films are a useful tool for quality assurance of tomotherapy treatments, as the treatment planning system may not give accurate dose values at the surface.

Intensity-modulated radiotherapy (IMRT)/Tomotherapy following neoadjuvant chemotherapy in stage IIB-IVA/B undifferentiated nasopharyngeal carcinomas (UCNT): a mono-institutional experience.

To evaluate the outcome of Undifferentiated Nasopharyngeal Carcinomas (UCNT) treated with intensity-modulated radiation therapy with Simultaneous Integrated Boost (SIB), following induction chemotherapy. Between January 2006 and June 2009, 52 patients with stage II B-IVA/B UCNT were treated either with linac-IMRT or Tomotherapy. All patients were scheduled to receive three cycles of cisplatin based neoadjuvant chemotherapy. With a median follow-up of 38.5 months (range 12.3-64.1), 3 year overall survival (OS), disease-free survival (DFS), and DFS by T2a-2b and T3-T4-stage were 95.0%, 84.6%, 89.0%, and 78.0%, respectively. At multivariate analysis, none of the examined prognostic factors reported statistical significance. N-classification was not a significant predictive factor for either OS or development of distant metastases. T-stage alone had a borderline effect on DFS and development of metastases. No difference between Tomotherapy and linac-IMRT emerged in terms of loco-regional control and development of severe, acute, and late toxicities. The most significant severe, acute toxicities were grade 3 (32.7%) and grade 4 (7.7%) mucositis. No grades 3 and 4 late toxicities were observed. The most commonly observed late effect was xerostomia, 11.5% patients complained grade 2 xerostomia. The severity of grade 2 xerostomia diminished over time with only four patients not improving salivation. IMRT-SIB following neoadjuvant chemotherapy was very satisfactory in terms of local control, regional control, DFS and OS rates in patients with stage IIB to IVB UCNT. In our experience, adding concurrent chemotherapy to IMRT after neoadjuvant chemotherapy in loco-regional widespread disease resulted to be the indicated approach.

A case of relapsed medulloblastoma treated with intensity-modulated radiotherapy and temozolomide.

We report a case of relapsed high-risk non-metastatic medulloblastoma in a 14-year-old boy, which was treated with intensity-modulated radiotherapy (IMRT) and temozolomide (TMZ). At the age of 11, the patient underwent an MRI-confirmed incomplete resection of a fourth-ventricle medulloblastoma, followed by conventional chemotherapy, craniospinal irradiation (55.8 Gy, 1.8 Gy/fraction) and then myeloablative chemotherapy followed by peripheral blood progenitor cell rescue. After 18 months of complete remission following the completion of chemotherapy, MRI showed a 2.5-cm mass in the olfactory notch. The patient underwent IMRT (45 Gy, 1.8 Gy/fraction) with concomitant administration of TMZ (180 mg/m2, 5 days every 21 days), which was well tolerated. After 5 cycles of TMZ, MRI showed complete remission with no evidence of the mass. TMZ was continued for another 5 cycles and then stopped. At 14 months from the completion of IMRT, a new MRI scan showed multiple nodular relapses around the fourth ventricle and the patient is currently treated with oral etoposide.

Optimizing craniospinal radiotherapy delivery in a pediatric patient affected by supratentorial PNET: a case report.

New advances in radiation therapy for children allow increased conformability and reduced doses to non-target tissues. We report our experience in treating a 4-year-old child with craniospinal tomotherapy after surgery of the primary tumor, a supratentorial primitive neuroectodermal tumor. The tomotherapy plan was compared with conventional craniospinal irradiation, 3D conformal radiation therapy, and intensity-modulated radiation therapy plans. The possible disadvantages of tomotherapy related to the radiation dose to organs at risk, treatment planning, and anesthesia should be carefully considered as the use of the technique is not suggested in a general manner, but selectively, in critical pediatric radiotherapy cases.

Correlation of a hypoxia based tumor control model with observed local control rates in nasopharyngeal carcinoma treated with chemoradiotherapy.

PURPOSE: To extend the application of current radiation therapy (RT) based tumor control probability (TCP) models of nasopharyngeal carcinoma (NPC) to include the effects of hypoxia and chemoradiotherapy (CRT). METHODS: A TCP model is described based on the linear-quadratic model modified to account for repopulation, chemotherapy, heterogeneity of dose to the tumor, and hypoxia. Sensitivity analysis was performed to determine which parameters exert the greatest influence on the uncertainty of modeled TCP. On the basis of the sensitivity analysis, the values of specific radiobiological parameters were set to nominal values reported in the literature for NPC or head and neck tumors. The remaining radiobiological parameters were determined by fitting TCP to clinical local control data from published randomized studies using both RT and CRT. Validation of the model was performed by comparison of estimated TCP and average overall local control rate (LCR) for 45 patients treated at the institution with conventional linear-accelerator-based or helical tomotherapy based intensity-modulated RT and neoadjuvant chemotherapy. RESULTS: Sensitivity analysis demonstrates that the model is most sensitive to the radiosensitivity term alpha and the dose per fraction. The estimated values of alpha and OER from data fitting were 0.396 Gy(-1) and 1.417. The model estimate of TCP (average 90.9%, range 26.9%-99.2%) showed good correlation with the LCR (86.7%). CONCLUSIONS: The model implemented in this work provides clinicians with a useful tool to predict the success rate of treatment, optimize treatment plans, and compare the effects of multimodality therapy.

Radiotherapy for patients with early-stage glottic carcinoma: univariate and multivariate analyses in a group of consecutive, unselected patients.

BACKGROUND: Radiotherapy (RT) has a remarkable success rate in the treatment of patients with glottic carcinoma. The objectives of the current study were to assess the results in a group of consecutive patients with comparable characteristics who were treated with RT (6-megavolt photon linear accelerator) and to determine the prognostic factors that may influence local control in patients with early-stage glottic carcinoma. The impact on local control of tobacco smoking and second primary malignancies also was investigated. METHODS: Four hundred ten patients with T1-T2 squamous cell carcinoma of the glottis who were treated between 1986 and 2001 were analyzed retrospectively with regard to local control and overall survival. Potential prognostic factors for local control were evaluated with univariate and multivariate models. The impact of technologic advances also was evaluated. RESULTS: The 5-year and 10-year overall survival rates were 83% and 63.5%, respectively. The overall 10-year local control rate for patients with T1-T2 glottic carcinoma was 89%. The median time to recurrence was 7 months. Univariate analysis showed that tumor category, tumor size, macroscopic appearance of the lesion, RT fraction size, persistent edema, year of RT treatment, unchanged dysphonia, and surgical option all had a significant influence on local control; whereas multivariate analysis showed that only persistent dysphonia and year of RT treatment were significantly associated with increased local control. A 22.2% rate of second primary malignancies was reported: second primary tumors were the major cause of death in the patients studied. Only 2 patients died of laryngeal carcinoma; 304 patients were alive with their disease in complete remission, 1 patient was alive with recurrent laryngeal carcinoma after undergoing salvage surgery, and 103 patients died of either intercurrent disease or a second primary tumor. CONCLUSIONS: The use of a 6-megavolt photon linear accelerator achieved a high rate of local control in patients with T1-T2 glottic carcinoma. Dysphonia and the year of RT treatment were the most important prognostically significant factors for patient outcome. The occurrence of a second primary tumor was the most frequent cause of death, especially among patients who did not stop smoking after a diagnosis of glottic carcinoma.