A decade comparison of regulatory decision patterns for oncology products to all other non-oncology products among Swissmedic, European Medicines Agency, and US Food and Drug Administration

Consensus of regulatory decisions on the same Marketing Authorization Application (MAA) are critical for stakeholders. In this context, regulatory decision patterns from the Swissmedic (SMC), the US Food and Drug Administration (FDA), and the European Medicines Agency (EMA) were analyzed for hemato-oncology products (OP) and non-oncology products (NOP). We compared 336 SMC regulatory decisions between 2009 and 2018 on new active substances with the EMA and the FDA for OP (n = 77) and NOP (n = 259) regarding approval rates, consensus, and divergent decisions. For OP MAA, we analyzed the underlying reasons for divergent decisions; for consensus decisions, the similarity and strictness of labeling. For OP, the approval rate for the SMC was 88.4%, the EMA 91.3%, and the FDA 95.7%. For NOP, the SMC had an approval rate of 86.2%, the EMA of 93.8%, and the FDA of 88.8%. The consensus decision rate among agencies was 88.4% for OP and 84.4% for NOP. The main clinical driver for divergent decisions for OP was nonrandomized trial design and low patient numbers. Comparing the approved indication wordings, the highest similarity was between the SMC and the EMA, and lowest for the FDA and the EMA. Investigating label strictness, the FDA numerically had the highest but not-statistically significant number of strict labels. The approval rate stratified by disease area (OP and NOP) differed among the SMC, the EMA, and the FDA. High concordance in regulatory decisions was observed between agencies for OP as well as NOP. Reasons for divergent decisions regarding OP were mainly due to scientific uncertainties. Comparing strictness of indications, numerical but no statistically significant differences were observed between agencies.

Targeting Treatment Resistance in Head and Neck Squamous Cell Carcinoma - Proof of Concept for CT Radiomics-Based Identification of Resistant Sub-Volumes

Purpose

Radiomics has already been proposed as a prognostic biomarker in head and neck cancer (HNSCC). However, its predictive power in radiotherapy has not yet been studied. Here, we investigated a local radiomics approach to distinguish between tumor sub-volumes with different levels of radiosensitivity as a possible target for radiation dose intensification.

Materials and Methods

Of 40 patients (n=28 training and n=12 validation) with biopsy confirmed locally recurrent HNSCC, pretreatment contrast-enhanced CT images were registered with follow-up PET/CT imaging allowing identification of controlled (GTVcontrol) vs non-controlled (GTVrec) tumor sub-volumes on pretreatment imaging. A bi-regional model was built using radiomic features extracted from pretreatment CT in the GTVrec and GTVcontrol to differentiate between those regions. Additionally, concept of local radiomics was implemented to perform detection task. The original tumor volume was divided into sub-volumes with no prior information on the location of recurrence. Radiomic features from those sub-volumes were then used to detect recurrent sub-volumes using multivariable logistic regression.

Results

Radiomic features extracted from non-controlled regions differed significantly from those in controlled regions (training AUC = 0.79 CI 95% 0.66 - 0.91 and validation AUC = 0.88 CI 95% 0.72 – 1.00). Local radiomics analysis allowed efficient detection of non-controlled sub-volumes both in the training AUC = 0.66 (CI 95% 0.56 – 0.75) and validation cohort 0.70 (CI 95% 0.53 – 0.86), however performance of this model was inferior to bi-regional model. Both models indicated that sub-volumes characterized by higher heterogeneity were linked to tumor recurrence.

Conclusion

Local radiomics is able to detect sub-volumes with decreased radiosensitivity, associated with location of tumor recurrence in HNSCC in the pre-treatment CT imaging. This proof of concept study, indicates that local CT radiomics can be used as predictive biomarker in radiotherapy and potential target for dose intensification.

Influence of inter-observer delineation variability on radiomics stability in different tumor sites

BACKGROUND

Radiomics is a promising methodology for quantitative analysis and description of radiological images using advanced mathematics and statistics. Tumor delineation, which is still often done manually, is an essential step in radiomics, however, inter-observer variability is a well-known uncertainty in radiation oncology. This study investigated the impact of inter-observer variability (IOV) in manual tumor delineation on the reliability of radiomic features (RF).

METHODS

Three different tumor types (head and neck squamous cell carcinoma (HNSCC), malignant pleural mesothelioma (MPM) and non-small cell lung cancer (NSCLC)) were included. For each site, eleven individual tumors were contoured on CT scans by three experienced radiation oncologists. Dice coefficients (DC) were calculated for quantification of delineation variability. RF were calculated with an in-house developed software implementation, which comprises 1404 features: shape (n = 18), histogram (n = 17), texture (n = 137) and wavelet (n = 1232). The IOV of RF was studied using the intraclass correlation coefficient (ICC). An ICC >0.8 indicates a good reproducibility. For the stable RF, an average linkage hierarchical clustering was performed to identify classes of uncorrelated features.

RESULTS

Median DC was high for NSCLC (0.86, range 0.57-0.90) and HNSCC (0.72, 0.21-0.89), whereas it was low for MPM (0.26, 0-0.9) indicating substantial IOV. Stability rate of RF correlated with DC and depended on tumor site, showing a high stability in NSCLC (90% of total parameters), acceptable stability in HNSCC (59% of total parameters) and low stability in MPM (36% of total parameters). Shape features showed the weakest stability across all tumor types. Hierarchical clustering revealed 14 groups of correlated and stable features for NSCLC and 6 groups for both HNSCC and MPM.

CONCLUSION

Inter-observer delineation variability has a relevant influence on radiomics analysis and is strongly influenced by tumor type. This leads to a reduced number of suitable imaging features.

[Not Available]

Zusammenfassung. Die Radiotherapie hat einen Stellenwert im Therapiealgorithmus auch bei nicht-malignen, benignen Erkrankungen der Gelenke und des Bindegewebes. Eine grosse Anzahl an degenerativen, entzündlichen, hyperproliferativen und funktionellen Erkrankungen kann mit gutem Erfolg durch eine Bestrahlung behandelt werden. Dabei ist diese Therapieform für den Patienten belastungs- und nebenwirkungsarm.

Lung and liver SBRT using helical tomotherapy--a dosimetric comparison of fixed jaw and dynamic jaw delivery

The purpose of the study was to evaluate the time effectiveness and dose distribution details of dynamic jaw delivery compared to the regular helical tomotherapy delivery mode in stereotactic body radiation therapy (SBRT) of liver and lung tumors. Ten patients with liver and ten patients with lung tumors were chosen to analyze the dose profiles and treatment times of regular helical tomotherapy delivery (2.5 cm field width) and new helical tomotherapy mode using dynamic jaw delivery with 5 cm field width. A median dose between 24 and 30 Gy was delivered in a single fraction. Regular helical tomotherapy took an average of 31.9 ± 6.7 min (lung SBRT) and 41.7 ± 15.0 min (liver SBRT). A reduction in delivery duration of 38.8% to 19.5 ± 2.9 min could be accomplished for lung irradiation (p < 0.05) and by 50.8% to 20.5 ± 6.0 min for liver SBRT (p < 0.05). Target coverage, as well as conformity and uniformity indices, showed no significant differences. No significant increase in organs‐at‐risk exposure could be detected either for lung or liver tumors. Therefore, use of new delivery mode with dynamic jaws improves treatment efficiency by reducing beam‐on time, while maintaining excellent plan quality.

PACS numbers: 87.55.D‐, 87.53.Ly, 87.55.N‐