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Introduction: Pancreatic adenocarcinoma (PAC) has some of the worst treatment outcomes for any solid tumor. PAC creates substantial difficulty for effective treatment with traditional RT delivery strategies primarily secondary to its location and limited visualization using CT. Several of these challenges are uniquely addressed with MR-guided RT. We sought to summarize and place into context the currently available literature on MR-guided RT specifically for PAC.
Methods: A literature search was conducted to identify manuscript publications since September 2014 that specifically used MR-guided RT for the treatment of PAC. Clinical outcomes of these series are summarized, discussed, and placed into the context of the existing pancreatic literature. Multiple international experts were involved to optimally contextualize these publications.
Results: Over 300 manuscripts were reviewed. A total of 6 clinical outcomes publications were identified that have treated patients with PAC using MR guidance. Successes, challenges, and future directions for this technology are evident in these publications. MR-guided RT holds theoretical promise for the treatment of patients with PAC. As with any new technology, immediate or dramatic clinical improvements associated with its use will take time and experience. There remain no prospective trials, currently publications are limited to small retrospective experiences. The current level of evidence for MR guidance in PAC is low and requires significant expansion. Future directions and ongoing studies that are currently open and accruing are identified and reviewed.
Conclusions: The potential promise of MR-guided RT for PAC is highlighted, the challenges associated with this novel therapeutic intervention are also reviewed. Outcomes are very early, and will require continued and long term follow up. MR-guided RT should not be viewed in the same fashion as a novel chemotherapeutic agent for which dosing, administration, and toxicity has been established in earlier phase studies. Instead, it should be viewed as a novel procedural intervention which must be robustly tested, refined and practiced before definitive conclusions on the potential benefits or detriments can be determined. The future of MR-guided RT for PAC is highly promising and the potential implications on PAC are substantial.
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(C) All elements of dates (except year) for dates that are directly related to an individual, including birth date, admission date, discharge date, death date, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older
Information that had previously been de-identified may still be adequately de-identified when the certification limit has been reached. When the certification timeframe reaches its conclusion, it does not imply that the data which has already been disseminated is no longer sufficiently protected in accordance with the de-identification standard. Covered entities will need to have an expert examine whether future releases of the data to the same recipient (e.g., monthly reporting) should be subject to additional or different de-identification processes consistent with current conditions to reach the very low risk requirement.
A second class of methods that can be applied for risk mitigation are based on generalization (sometimes referred to as abbreviation) of the information. These methods transform data into more abstract representations. For instance, a five-digit ZIP Code may be generalized to a four-digit ZIP Code, which in turn may be generalized to a three-digit ZIP Code, and onward so as to disclose data with lesser degrees of granularity. Similarly, the age of a patient may be generalized from one- to five-year age groups. Table 4 illustrates how generalization (i.e., gray shaded cells) might be applied to the information in Table 2.
In line with this guidance from NIST, a covered entity may disclose codes derived from PHI as part of a de-identified data set if an expert determines that the data meets the de-identification requirements at 164.514(b)(1). The re-identification provision in 164.514(c) does not preclude the transformation of PHI into values derived by cryptographic hash functions using the expert determination method, provided the keys associated with such functions are not disclosed, including to the recipients of the de-identified information.
No. The Privacy Rule does not limit how a covered entity may disclose information that has been de-identified. However, a covered entity may require the recipient of de-identified information to enter into a data use agreement to access files with known disclosure risk, such as is required for release of a limited data set under the Privacy Rule. This agreement may contain a number of clauses designed to protect the data, such as prohibiting re-identification.30 Of course, the use of a data use agreement does not substitute for any of the specific requirements of the Expert Determination Method. Further information about data use agreements can be found on the OCR website.31 Covered entities may make their own assessments whether such additional oversight is appropriate.
ZCTAs are generalized area representations of U.S. Postal Service (USPS) ZIP code service areas. Simply put, each one is built by aggregating the Census 2000 blocks, whose addresses use a given ZIP code, into a ZCTA which gets that ZIP code assigned as its ZCTA code. They represent the majority USPS five-digit ZIP code found in a given area. For those areas where it is difficult to determine the prevailing five-digit ZIP code, the higher-level three-digit ZIP code is used for the ZCTA code. For further information, go to: -surveys/geography/guidance/geo-areas/zctas.html
No. The Privacy Rule does not limit how a covered entity may disclose information that has been de-identified. However, nothing prevents a covered entity from asking a recipient of de-identified information to enter into a data use agreement, such as is required for release of a limited data set under the Privacy Rule. This agreement may prohibit re-identification. Of course, the use of a data use agreement does not substitute for any of the specific requirements of the Safe Harbor method. Further information about data use agreements can be found on the OCR website.36 Covered entities may make their own assessments whether such additional oversight is appropriate.
The de-identification standard makes no distinction between data entered into standardized fields and information entered as free text (i.e., structured and unstructured text) -- an identifier listed in the Safe Harbor standard must be removed regardless of its location in a record if it is recognizable as an identifier.
Information that is a subset of health information, including demographic information collected from an individual, and:(1) Is created or received by a health care provider, health plan, employer, or health care clearinghouse; and(2) Relates to the past, present, or future physical or mental health or condition of an individual; the provision of health care to an individual; or the past, present, or future payment for the provision of health care to the individual; and(i) That identifies the individual; or(ii) With respect to which there is a reasonable basis to believe the information can be used to identify the individual.
The four relative sea level rise (RSL) scenarios shown in this tab are derived from the 2022 Sea Level Rise Technical Report using the same methods as the U.S. Army Corps of Engineers Sea Level Change Curve Calculator. These new scenarios were developed by the U.S. Sea Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force as input into the U.S. Global Change Research Program Sustained Assessment process and, Fifth National Climate Assessment. These RSL scenarios provide an update to the NOAA 2017 scenarios, which were developed as input to the Fourth National Climate Assessment.
The inundation areas depicted in the Sea Level Rise tab are not as precise as they may appear. There are many unknowns when mapping future conditions, including natural evolution of the coastal landforms (e.g., barrier island overwash and migration), as well as the data used to predict the changes. The presentation of confidence in these maps only represents the known error in the elevation data and tidal corrections.