Teaching cancer imaging in the era of precision medicine: Looking at the big picture

Training radiology residents to evaluate deep myometrial invasion in endometrial cancer patients on MRI: A learning curve study

PURPOSE

To evaluate the impact of a four-month training program on radiology residents' diagnostic accuracy in assessing deep myometrial invasion (DMI) in endometrial cancer (EC) using MRI.

METHOD

Three radiology residents with limited EC MRI experience participated in the training program, which included conventional didactic sessions, case-centric workshops, and interactive classes. Utilizing a training dataset of 120 EC MRI scans, trainees independently assessed subsets of cases over five reading sessions. Each subset consisted of 30 scans, the first and the last with the same cases, for a total of 150 reads. Diagnostic accuracy metrics, assessment time (rounded to the nearest minute), and confidence levels (using a 5-point Likert scale) were recorded. The learning curve was obtained plotting the diagnostic accuracy of the three trainees and the average over the subsets. Anatomopathological results served as the reference standard for DMI presence.

RESULTS

The three trainees exhibited heterogeneous starting point, with a learning curve and a trend to more homogeneous performance with training. The diagnostic accuracy of the average trainee raised from 64 % (56 %-76 %) to 88 % (80 %-94 %) across the five subsets (p < 0.001). Reductions in assessment time (5.92 to 4.63 min, p < 0.018) and enhanced confidence levels (3.58 to 3.97, p = 0.12) were observed. Improvements in sensitivity, specificity, positive predictive value, and negative predictive value were noted, particularly for specificity which raised from 56 % (41 %-68 %) in the first to 86 % (74 %-94 %) in the fifth subset (p = 0.16). Although not reaching statistical significance, these advancements aligned the trainees with literature performance benchmarks.

CONCLUSIONS

The structured training program significantly enhanced radiology residents' diagnostic accuracy in assessing DMI for EC on MRI, emphasizing the effectiveness of active case-based training in refining oncologic imaging skills within radiology residency curricula.

Aptamer-functionalized two-photon SiO2@GQDs hybrid-based signal amplification strategy for targeted cancer imaging

Targeted imaging is playing an increasingly important role in the early detection and precise diagnosis of cancer. This need has motivated research into sensory nanomaterials that can be constructed into imaging agents to serve as biosensors. Graphene quantum dots (GQDs) as a valuable nanoprobe show great potential for use in two-photon biological imaging. However, most as-prepared GQDs exhibit a low two-photon absorption cross-section, narrow spectral coverage, and "one-to-one" signal conversion mode, which greatly hamper their wide application in sensitive early-stage cancer detection. Herein, a versatile strategy has been employed to fabricate an aptamer Sgc8c-functionalized hybrid as a proof-of-concept of the signal amplification strategy for targeted cancer imaging. In this study, GQDs with two-photon imaging performance, and silica nanoparticles (SiO2 NPs) as nanocarriers to provide amplified recognition events by high loading of GQD signal tags, were adopted to construct a two-photon hybrid-based signal amplification strategy. Thus, the obtained hybrid (denoted SiO2@GQDs) enabled extremely strong fluorescence with a quantum yield up to 0.49, excellent photostability and biocompatibility, and enhanced bright two-photon fluorescence up to 2.7 times that of bare GQDs (excitation at 760 nm; emission at 512 nm). Moreover, further modification with aptamer Sgc8c showed little disruption to the structure of the SiO2@GQDs-hybrid and the corresponding two-photon emission. Hence, SiO2@GQDs-Sgc8c showed specific responses to target cells. Moreover, it could be used as a signal-amplifying two-photon nanoprobe for targeted cancer imaging with high specificity and great efficiency, which exhibits a distinct green fluorescence compared to that of GQDs-Sgc8c or SiO2@GQDs. This signal amplification strategy holds great potential for the accurate early diagnosis of tumors and offers new tools for the detection a wide variety of analytes in clinical application.

Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field

OBJECTIVES

To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions.

DATA SOURCES

Peer-reviewed scientific publications and expert opinion.

CONCLUSION

The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services.

IMPLICATIONS FOR NURSING PRACTICE

As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.